GPT-5.4 Nano
GPT-5.4 Nano is the smallest and fastest variant of GPT-5.4, designed for high-throughput, low-latency tasks such as classification, data extraction, ranking, and lightweight automation. It prioritizes speed and cost efficiency for simple, high-volume workloads and is available exclusively via the API.
API Options
Platform-level options for task execution and delivery.
-
taskType
string required value: textInference -
Identifier for the type of task being performed
-
taskUUID
string required UUID v4 -
UUID v4 identifier for tracking tasks and matching async responses. Must be unique per task.
-
webhookURL
string URI -
Specifies a webhook URL where JSON responses will be sent via HTTP POST when generation tasks complete. For batch requests with multiple results, each completed item triggers a separate webhook call as it becomes available.
Learn more 1 resource
- Webhooks PLATFORM
- Webhooks
-
deliveryMethod
string default: sync -
Determines how the API delivers task results.
Allowed values 3 values
- Returns complete results directly in the API response.
- Returns an immediate acknowledgment with the task UUID. Poll for results using getResponse.
- Streams results token-by-token as they are generated.
Learn more 1 resource
- Task Responses PLATFORM
-
includeCost
boolean default: true -
Include task cost in the response.
-
numberResults
integer min: 1 max: 4 default: 1 -
Number of results to generate. Each result uses a different seed, producing variations of the same parameters.
-
includeUsage
boolean default: false -
Include token usage statistics in the response.
Inputs
Input resources for the task (images, audio, etc). These must be nested inside the inputs object.
inputs object.-
inputs»imagesimages
array of strings min items: 1 -
Array of image inputs (UUID, URL, Data URI, or Base64).
Generation Parameters
Core parameters for controlling the generated content.
-
model
string required value: openai:gpt@5.4-nano -
Identifier of the model to use for generation.
Learn more 3 resources
-
seed
integer min: 0 max: 9223372036854776000 -
Random seed for reproducible generation. When not provided, a random seed is generated in the unsigned 32-bit range.
Learn more 1 resource
-
messages
array of objects required min items: 1 -
Array of chat messages forming the conversation context.
Settings
Technical parameters to fine-tune the inference process. These must be nested inside the settings object.
settings object.-
settings»systemPromptsystemPrompt
string min: 1 max: 200000 -
System-level instruction that guides the model's behavior and output style across the entire generation.
-
settings»temperaturetemperature
float min: 0 max: 2 step: 0.01 default: 1 -
Controls randomness in generation. Lower values produce more deterministic outputs, higher values increase variation and creativity.
-
settings»topPtopP
float min: 0 max: 1 step: 0.01 default: 0.95 -
Nucleus sampling parameter that controls diversity by limiting the probability mass. Lower values make outputs more focused, higher values increase diversity.
-
settings»maxTokensmaxTokens
integer min: 1 max: 128000 default: 4096 -
Maximum number of tokens to generate in the response.
-
settings»thinkingLevelthinkingLevel
string -
Controls the depth of internal reasoning the model performs before generating a response.
Allowed values 4 values
-
settings»toolChoicetoolChoice
object -
Controls how the model selects which tool to call.
Properties 2 properties
-
settings»toolChoice»typetype
string required -
Tool selection strategy.
Allowed values 4 values
- Model decides whether to call a tool.
- Model must call at least one tool.
- Model must call the specified tool.
- Model must not call any tool.
-
settings»toolChoice»namename
string -
Name of the tool to call.
-
-
settings»toolstools
array of objects -
Function definitions available for the model to call. Each tool is a JSON Schema object describing the function signature.
Foundry Noticeboard Error Scan
## 1) Document types present
- **Maintenance document(s)** (one clearly labeled **“MAINTENANCE”** on the upper-right sheet; includes a **QR code**). *(Other sheets appear to be maintenance/inspection logs or tables, but the text is not legible enough to confirm exact titles.)*
- **Inspection / test record sheets** with **table grids** and **handwritten red entries** across multiple pages. *(Exact document names/titles are uncertain.)*
- **Operational measurement / monitoring charts** (appear to be tabular measurement results with repeated rows/columns). *(Title uncertain.)*
- **Visual annotation overlays** (tape/covered areas, highlighted blocks in **yellow**, and several **colored sticky-note flags**: yellow/orange, blue, pink, and others).
---
## 2) Key identifiers and dates
- **Document label:** “**MAINTENANCE**” (upper-right).
- **QR code:** present on the “MAINTENANCE” sheet. *(The linked content/asset ID is unreadable.)*
- **Specific equipment IDs / asset numbers:** **Uncertain** (not readable at this resolution).
- **Dates / due dates / inspection cycle dates:** **Uncertain** (date fields are present on some tables, but the numbers are not legible enough to extract reliably).
- **Handwritten red entries:** appear to include **dated/numbered records**, but the exact values/dates cannot be read.
---
## 3) Warnings or urgent issues
- **High risk of audit/compliance gaps** because:
- Multiple key documents have **handwritten red entries** over printed tables, but the **critical fields (dates, readings, responsible person/sign-off, pass/fail status)** are not legible in the image.
- The board contains **tightly annotated measurement grids**; if any are incorrect/overwritten without clear traceability, it can invalidate the record.
- **Overdue inspections flag:** **Cannot be confirmed** from the image because the **inspection due dates** and **last completed dates** are not readable.
---
## 4) Handwritten edits and overrides
- **Extensive handwritten red markings** on at least **two central/right sheets**:
- Looks like **values written into table cells**, plus **cross-outs/overwrites** and **additional annotations**.
- Whether these edits represent **corrections, replacements, or new entries** is **uncertain** (no legible “revised by / reason / initialed” text visible).
- **Colored sticky notes** placed over/near specific sections:
- Yellow/orange, blue, and pink notes appear to **mark items of attention**, but the text on the notes is **not readable**.
- **Yellow highlight blocks** on at least one lower-right section:
- Suggests emphasis or flagged results, but the underlying meaning is **uncertain**.
---
## 5) Missing, conflicting, or suspicious details
- **Missing/illegible required fields (uncertain which are absent vs. unreadable):**
- Equipment/line/unit identifiers
- Inspection dates and due dates
- Acceptance criteria (e.g., limits/pass-fail)
- Signatures/initials of inspector/approver
- **Potential conflict risk (not provable from this image):**
- Handwritten red entries **appear to replace or augment printed table data**. Without legible comparison, **contradictions between printed values and handwritten overrides cannot be confirmed**.
- **Suspicious documentation handling (based on visible artifacts):**
- Tape/covering and heavy marking suggest **active revisions**, but traceability (revision control notes) is **unreadable**.
---
## 6) Final 5-bullet action summary
- **Re-capture a higher-resolution photo** (or zoom shots) focusing on **dates, asset IDs, and pass/fail columns**—especially on the sheets with the most red handwritten edits.
- **Verify inspection compliance** by extracting: **last completed date vs. due date** for each referenced check item (currently **not confirmable**).
- **Audit handwriting traceability:** confirm whether red entries are **official corrections** (with initials/revision reason) or informal notes (currently **uncertain**).
- **Ensure sign-off completeness:** locate and confirm **inspector/approver names/initials** are present and legible on each maintenance/inspection record.
- **Address flagged areas on the board** (yellow highlighted sections and colored sticky-note marks) by checking whether they correspond to **out-of-tolerance results** or **open actions** (meaning **uncertain** from this image). stop {
"taskType": "textInference",
"taskUUID": "595dcbb6-ed9a-4197-940f-4233947715e6",
"model": "openai:gpt@5.4-nano",
"seed": 83712,
"settings": {
"systemPrompt": "You are a fast, precise operations-document parser. Analyze the provided image and extract all readable operational information. Prioritize accuracy, concise wording, and complete coverage. If text is unclear, mark it as uncertain instead of guessing. Organize the response into these sections: 1) Document types present, 2) Key identifiers and dates, 3) Warnings or urgent issues, 4) Handwritten edits and overrides, 5) Missing, conflicting, or suspicious details, 6) A final 5-bullet action summary.",
"temperature": 0.2,
"topP": 0.9,
"maxTokens": 12000,
"thinkingLevel": "medium"
},
"includeUsage": true,
"inputs": {
"images": [
"https://assets.runware.ai/assets/inputs/9788b2f9-5c0a-4cd0-b184-191b7b1ccc83.jpg"
]
},
"messages": [
{
"role": "user",
"content": "Review this noticeboard image from a metalworking floor. Extract the readable information, identify any contradictions between printed sheets and handwritten edits, flag overdue inspections, and summarize the top operational risks. Keep the answer structured under the six requested headings."
}
]
}{
"taskType": "textInference",
"taskUUID": "595dcbb6-ed9a-4197-940f-4233947715e6",
"text": "## 1) Document types present\n- **Maintenance document(s)** (one clearly labeled **“MAINTENANCE”** on the upper-right sheet; includes a **QR code**). *(Other sheets appear to be maintenance/inspection logs or tables, but the text is not legible enough to confirm exact titles.)*\n- **Inspection / test record sheets** with **table grids** and **handwritten red entries** across multiple pages. *(Exact document names/titles are uncertain.)*\n- **Operational measurement / monitoring charts** (appear to be tabular measurement results with repeated rows/columns). *(Title uncertain.)*\n- **Visual annotation overlays** (tape/covered areas, highlighted blocks in **yellow**, and several **colored sticky-note flags**: yellow/orange, blue, pink, and others).\n\n---\n\n## 2) Key identifiers and dates\n- **Document label:** “**MAINTENANCE**” (upper-right). \n- **QR code:** present on the “MAINTENANCE” sheet. *(The linked content/asset ID is unreadable.)*\n- **Specific equipment IDs / asset numbers:** **Uncertain** (not readable at this resolution).\n- **Dates / due dates / inspection cycle dates:** **Uncertain** (date fields are present on some tables, but the numbers are not legible enough to extract reliably).\n- **Handwritten red entries:** appear to include **dated/numbered records**, but the exact values/dates cannot be read.\n\n---\n\n## 3) Warnings or urgent issues\n- **High risk of audit/compliance gaps** because:\n - Multiple key documents have **handwritten red entries** over printed tables, but the **critical fields (dates, readings, responsible person/sign-off, pass/fail status)** are not legible in the image.\n - The board contains **tightly annotated measurement grids**; if any are incorrect/overwritten without clear traceability, it can invalidate the record.\n- **Overdue inspections flag:** **Cannot be confirmed** from the image because the **inspection due dates** and **last completed dates** are not readable.\n\n---\n\n## 4) Handwritten edits and overrides\n- **Extensive handwritten red markings** on at least **two central/right sheets**:\n - Looks like **values written into table cells**, plus **cross-outs/overwrites** and **additional annotations**.\n - Whether these edits represent **corrections, replacements, or new entries** is **uncertain** (no legible “revised by / reason / initialed” text visible).\n- **Colored sticky notes** placed over/near specific sections:\n - Yellow/orange, blue, and pink notes appear to **mark items of attention**, but the text on the notes is **not readable**.\n- **Yellow highlight blocks** on at least one lower-right section:\n - Suggests emphasis or flagged results, but the underlying meaning is **uncertain**.\n\n---\n\n## 5) Missing, conflicting, or suspicious details\n- **Missing/illegible required fields (uncertain which are absent vs. unreadable):**\n - Equipment/line/unit identifiers\n - Inspection dates and due dates\n - Acceptance criteria (e.g., limits/pass-fail)\n - Signatures/initials of inspector/approver\n- **Potential conflict risk (not provable from this image):**\n - Handwritten red entries **appear to replace or augment printed table data**. Without legible comparison, **contradictions between printed values and handwritten overrides cannot be confirmed**.\n- **Suspicious documentation handling (based on visible artifacts):**\n - Tape/covering and heavy marking suggest **active revisions**, but traceability (revision control notes) is **unreadable**.\n\n---\n\n## 6) Final 5-bullet action summary\n- **Re-capture a higher-resolution photo** (or zoom shots) focusing on **dates, asset IDs, and pass/fail columns**—especially on the sheets with the most red handwritten edits.\n- **Verify inspection compliance** by extracting: **last completed date vs. due date** for each referenced check item (currently **not confirmable**).\n- **Audit handwriting traceability:** confirm whether red entries are **official corrections** (with initials/revision reason) or informal notes (currently **uncertain**).\n- **Ensure sign-off completeness:** locate and confirm **inspector/approver names/initials** are present and legible on each maintenance/inspection record.\n- **Address flagged areas on the board** (yellow highlighted sections and colored sticky-note marks) by checking whether they correspond to **out-of-tolerance results** or **open actions** (meaning **uncertain** from this image).",
"seed": 83712,
"cost": 0.00186,
"finishReason": "stop",
"usage": {
"promptTokens": 1271,
"completionTokens": 1287,
"totalTokens": 2558,
"completionTokensDetails": {
"reasoningTokens": 309
},
"promptTokensDetails": {
"cachedTokens": 0
}
}
}Warehouse Invoice Triage Queue
{
"summary": {
"document_count": 4,
"currencies_detected": [],
"highest_total": null,
"flags": [
"unreadable_region",
"handwriting_present",
"missing_field"
]
},
"documents": [
{
"vendor_name": null,
"invoice_number": null,
"purchase_order": null,
"invoice_date": null,
"due_date": null,
"currency": null,
"subtotal": null,
"tax": null,
"total": null,
"line_item_count": null,
"confidence": 0.12
},
{
"vendor_name": null,
"invoice_number": null,
"purchase_order": null,
"invoice_date": null,
"due_date": null,
"currency": null,
"subtotal": null,
"tax": null,
"total": null,
"line_item_count": null,
"confidence": 0.12
},
{
"vendor_name": null,
"invoice_number": null,
"purchase_order": null,
"invoice_date": null,
"due_date": null,
"currency": null,
"subtotal": null,
"tax": null,
"total": null,
"line_item_count": null,
"confidence": 0.12
},
{
"vendor_name": null,
"invoice_number": null,
"purchase_order": null,
"invoice_date": null,
"due_date": null,
"currency": null,
"subtotal": null,
"tax": null,
"total": null,
"line_item_count": null,
"confidence": 0.1
}
]
} stop {
"taskType": "textInference",
"taskUUID": "e3e532d4-b6ea-41ee-8f15-2291b901f732",
"model": "openai:gpt@5.4-nano",
"seed": 49394,
"settings": {
"systemPrompt": "You are a fast document-processing assistant optimized for extracting structured data from operational paperwork. Read the provided image carefully and return only valid JSON. If multiple documents are visible, process each one separately. For every document, extract: vendor_name, invoice_number, purchase_order, invoice_date, due_date, currency, subtotal, tax, total, line_item_count, and confidence. Also include a top-level summary with document_count, currencies_detected, highest_total, and any flags such as missing_field, unreadable_region, duplicate_invoice_number, or handwriting_present. Use null for missing values. Do not include markdown or explanatory text.",
"temperature": 0.1,
"topP": 0.9,
"maxTokens": 12000,
"thinkingLevel": "medium"
},
"inputs": {
"images": [
"https://assets.runware.ai/assets/inputs/58747a6a-c347-4e5d-93ee-f965a0372d23.jpg"
]
},
"includeUsage": true,
"messages": [
{
"role": "user",
"content": "Extract all visible invoice data from this image and output strict JSON with the schema described in the system instructions. Prioritize accuracy, preserve formatting for invoice numbers and purchase orders, and include every visible document even if partial."
}
]
}{
"taskType": "textInference",
"taskUUID": "e3e532d4-b6ea-41ee-8f15-2291b901f732",
"text": "{\n \"summary\": {\n \"document_count\": 4,\n \"currencies_detected\": [],\n \"highest_total\": null,\n \"flags\": [\n \"unreadable_region\",\n \"handwriting_present\",\n \"missing_field\"\n ]\n },\n \"documents\": [\n {\n \"vendor_name\": null,\n \"invoice_number\": null,\n \"purchase_order\": null,\n \"invoice_date\": null,\n \"due_date\": null,\n \"currency\": null,\n \"subtotal\": null,\n \"tax\": null,\n \"total\": null,\n \"line_item_count\": null,\n \"confidence\": 0.12\n },\n {\n \"vendor_name\": null,\n \"invoice_number\": null,\n \"purchase_order\": null,\n \"invoice_date\": null,\n \"due_date\": null,\n \"currency\": null,\n \"subtotal\": null,\n \"tax\": null,\n \"total\": null,\n \"line_item_count\": null,\n \"confidence\": 0.12\n },\n {\n \"vendor_name\": null,\n \"invoice_number\": null,\n \"purchase_order\": null,\n \"invoice_date\": null,\n \"due_date\": null,\n \"currency\": null,\n \"subtotal\": null,\n \"tax\": null,\n \"total\": null,\n \"line_item_count\": null,\n \"confidence\": 0.12\n },\n {\n \"vendor_name\": null,\n \"invoice_number\": null,\n \"purchase_order\": null,\n \"invoice_date\": null,\n \"due_date\": null,\n \"currency\": null,\n \"subtotal\": null,\n \"tax\": null,\n \"total\": null,\n \"line_item_count\": null,\n \"confidence\": 0.1\n }\n ]\n}",
"seed": 49394,
"cost": 0.00133,
"finishReason": "stop",
"usage": {
"promptTokens": 1619,
"completionTokens": 802,
"totalTokens": 2421,
"completionTokensDetails": {
"reasoningTokens": 412
},
"promptTokensDetails": {
"cachedTokens": 0
}
}
}Freight Label Exception Sorting
recipient_name: "Recipient Unit J" (rest unclear)
destination_address: "AM700C; TRANBRANGORRA ?; RLGT04?OH ORHMO ?; BAISTNAVI?ON TO BORER A1 (all other characters unclear/uncertain)"
tracking_number: "TRACKON: 3:2931602228" (uncertain, partially unclear)
service_level: "SERVICE LEVEL: 092?325 BOXES 125110" (uncertain)
package_weight: "063 3000" (unit unclear; likely weight)
special_handling_notes: "HOLD" (handwritten/marker on label); red sticker: "DAMAGE …" (exact wording unclear)
manual_review_flag: YES
review_reason: "HOLD and a damage/handling sticker are present, and several address/service/weight fields are not fully legible—requires human verification." stop {
"taskType": "textInference",
"taskUUID": "cce180b7-7bb5-42df-841e-1168b9390928",
"model": "openai:gpt@5.4-nano",
"seed": 12487,
"settings": {
"systemPrompt": "You are a fast logistics document assistant optimized for concise extraction, classification, and operational exception detection. Read package-label images carefully. If some text is unclear, mark it as uncertain instead of inventing details. Prefer compact structured plain text.",
"temperature": 0.2,
"topP": 0.9,
"maxTokens": 12000,
"thinkingLevel": "medium"
},
"includeUsage": true,
"inputs": {
"images": [
"https://assets.runware.ai/assets/inputs/ab8bb7d6-7df1-4358-9b58-a450a7edca87.jpg"
]
},
"messages": [
{
"role": "user",
"content": "Analyze the shipping label in this image and provide: 1) recipient name, 2) full destination address, 3) tracking number, 4) service level, 5) package weight, 6) any handwritten or sticker-based special handling notes, 7) whether the shipment should be flagged for manual review. Then add a short reason for the review decision. Format the answer as compact field-value lines."
}
]
}{
"taskType": "textInference",
"taskUUID": "cce180b7-7bb5-42df-841e-1168b9390928",
"text": "recipient_name: \"Recipient Unit J\" (rest unclear) \ndestination_address: \"AM700C; TRANBRANGORRA ?; RLGT04?OH ORHMO ?; BAISTNAVI?ON TO BORER A1 (all other characters unclear/uncertain)\" \ntracking_number: \"TRACKON: 3:2931602228\" (uncertain, partially unclear) \nservice_level: \"SERVICE LEVEL: 092?325 BOXES 125110\" (uncertain) \npackage_weight: \"063 3000\" (unit unclear; likely weight) \nspecial_handling_notes: \"HOLD\" (handwritten/marker on label); red sticker: \"DAMAGE …\" (exact wording unclear) \nmanual_review_flag: YES \n\nreview_reason: \"HOLD and a damage/handling sticker are present, and several address/service/weight fields are not fully legible—requires human verification.\"",
"seed": 12487,
"cost": 0.00149,
"finishReason": "stop",
"usage": {
"promptTokens": 1064,
"completionTokens": 1019,
"totalTokens": 2083,
"completionTokensDetails": {
"reasoningTokens": 823
},
"promptTokensDetails": {
"cachedTokens": 0
}
}
}Municipal Bench Plaque Audit
## Bench/plaque OCR results (visible in this image)
### Plaque 1
- **Approx. location:** Right side, dark bench closest to camera; **top plaque** (near upper-right corner)
- **Transcribed text (best effort):**
`EMILIANO GAZZATI[N?]`
`...`
`CONSOLAZIONE`
`...`
`1920?–19??`
*(Multiple lines are present but most characters are too small/blurry to read reliably.)*
- **Confidence:** low
- **Normalized dedication name:** **“Emiliano Gazzati[n?]”**
---
### Plaque 2
- **Approx. location:** Right side, same dark bench; **middle plaque** (just below Plaque 1)
- **Transcribed text (best effort):**
`GIAC[?] CI[?]DERNI`
`...`
`...`
- **Confidence:** low
- **Normalized dedication name:** **“Giac[?] Ciderni”**
---
### Plaque 3
- **Approx. location:** Right side, same dark bench; **lower plaque** (just below Plaque 2)
- **Transcribed text (best effort):**
`DORA MEL[?]ON`
`...`
`1940?–19??`
- **Confidence:** low
- **Normalized dedication name:** **“Dora Mel[?]on”**
---
### Plaque 4
- **Approx. location:** Foreground light bench (center-right); one of the **left/upper plaques on the backrest rail** (gold plate near the middle of the image)
- **Transcribed text (best effort):**
`E? EL[?]UNGAOR`
`...`
`...`
- **Confidence:** low
- **Normalized dedication name:** **“E?[?] El[?]ung[a]or”**
---
### Plaque 5
- **Approx. location:** Foreground light bench (center); **small gold plaque on the backrest rail** (mid-frame, slightly left of center)
- **Transcribed text (best effort):**
`G?ND? GRIFFITH[?]`
`...`
- **Confidence:** low
- **Normalized dedication name:** **“G?nd? Griffith[?]”**
---
### Plaque 6
- **Approx. location:** Foreground light bench (center-bottom); **small gold plaque on the bench slat** (most forward plaque on the light bench)
- **Transcribed text (best effort):**
`...`
*(Text is present but too small/blurred in this view to transcribe reliably.)*
- **Confidence:** low
- **Normalized dedication name:** **None readable**
---
## Plaques likely present but require manual recheck
These appear as gold/brass rectangles on additional benches, but the inscriptions are too small and/or obscured by distance, angle, or shadow to OCR confidently:
1. **Back-row benches (left/middle background):** multiple benches with brass plaques along the row; inscriptions are not legible at this resolution.
2. **Additional plaques on the foreground light bench:** if there are more than the 2–3 clearly identifiable plates on the slat/backrest, their text is not readable enough to confirm.
3. **Any plaques on the dark bench beyond Plaque 1–3:** edges/angles make characters uncertain.
If you can upload a higher-resolution crop of each plaque (or a closer photo of the right-side dark bench and the foreground light bench), I can re-OCR and raise confidence. stop {
"taskType": "textInference",
"taskUUID": "df039aa4-d093-499d-bdf8-0a93f64a9c93",
"model": "openai:gpt@5.4-nano",
"seed": 72273,
"settings": {
"systemPrompt": "You are a fast municipal records assistant. Extract visible plaque text from the provided image with best-effort OCR, then organize the results clearly. If text is uncertain, preserve the uncertain characters with [?]. Do not invent details not supported by the image. Return concise but structured output with one item per bench or plaque region.",
"temperature": 0.2,
"topP": 0.9,
"maxTokens": 12000,
"thinkingLevel": "low"
},
"includeUsage": true,
"inputs": {
"images": [
"https://assets.runware.ai/assets/inputs/a173496f-73bd-4736-9df0-53d3230eba93.jpg"
]
},
"messages": [
{
"role": "user",
"content": "Analyze the uploaded park image. Identify every bench plaque you can find. For each plaque, provide: 1) plaque index, 2) approximate location in the image, 3) transcribed text, 4) confidence level as high/medium/low, and 5) a short normalization of the dedication name if present. Then add a final section listing plaques that may require a manual recheck because of glare, angle, distance, or occlusion."
}
]
}{
"taskType": "textInference",
"taskUUID": "df039aa4-d093-499d-bdf8-0a93f64a9c93",
"text": "## Bench/plaque OCR results (visible in this image)\n\n### Plaque 1\n- **Approx. location:** Right side, dark bench closest to camera; **top plaque** (near upper-right corner)\n- **Transcribed text (best effort):** \n `EMILIANO GAZZATI[N?]` \n `...` \n `CONSOLAZIONE` \n `...` \n `1920?–19??` \n *(Multiple lines are present but most characters are too small/blurry to read reliably.)*\n- **Confidence:** low\n- **Normalized dedication name:** **“Emiliano Gazzati[n?]”**\n\n---\n\n### Plaque 2\n- **Approx. location:** Right side, same dark bench; **middle plaque** (just below Plaque 1)\n- **Transcribed text (best effort):** \n `GIAC[?] CI[?]DERNI` \n `...` \n `...` \n- **Confidence:** low\n- **Normalized dedication name:** **“Giac[?] Ciderni”**\n\n---\n\n### Plaque 3\n- **Approx. location:** Right side, same dark bench; **lower plaque** (just below Plaque 2)\n- **Transcribed text (best effort):** \n `DORA MEL[?]ON` \n `...` \n `1940?–19??` \n- **Confidence:** low\n- **Normalized dedication name:** **“Dora Mel[?]on”**\n\n---\n\n### Plaque 4\n- **Approx. location:** Foreground light bench (center-right); one of the **left/upper plaques on the backrest rail** (gold plate near the middle of the image)\n- **Transcribed text (best effort):** \n `E? EL[?]UNGAOR` \n `...` \n `...` \n- **Confidence:** low\n- **Normalized dedication name:** **“E?[?] El[?]ung[a]or”**\n\n---\n\n### Plaque 5\n- **Approx. location:** Foreground light bench (center); **small gold plaque on the backrest rail** (mid-frame, slightly left of center)\n- **Transcribed text (best effort):** \n `G?ND? GRIFFITH[?]` \n `...` \n- **Confidence:** low\n- **Normalized dedication name:** **“G?nd? Griffith[?]”**\n\n---\n\n### Plaque 6\n- **Approx. location:** Foreground light bench (center-bottom); **small gold plaque on the bench slat** (most forward plaque on the light bench)\n- **Transcribed text (best effort):** \n `...` \n *(Text is present but too small/blurred in this view to transcribe reliably.)*\n- **Confidence:** low\n- **Normalized dedication name:** **None readable**\n\n---\n\n## Plaques likely present but require manual recheck\nThese appear as gold/brass rectangles on additional benches, but the inscriptions are too small and/or obscured by distance, angle, or shadow to OCR confidently:\n\n1. **Back-row benches (left/middle background):** multiple benches with brass plaques along the row; inscriptions are not legible at this resolution.\n2. **Additional plaques on the foreground light bench:** if there are more than the 2–3 clearly identifiable plates on the slat/backrest, their text is not readable enough to confirm.\n3. **Any plaques on the dark bench beyond Plaque 1–3:** edges/angles make characters uncertain.\n\nIf you can upload a higher-resolution crop of each plaque (or a closer photo of the right-side dark bench and the foreground light bench), I can re-OCR and raise confidence.",
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