Triple
T419505
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Farm Security Administration photographers |
E8068
|
entity |
| Predicate | numberOfImages |
P425
|
FINISHED |
| Object | over 170000 black-and-white photographs |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: over 170000 black-and-white photographs | Statement: [Farm Security Administration photographers, numberOfImages, over 170000 black-and-white photographs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfImages Context triple: [Farm Security Administration photographers, numberOfImages, over 170000 black-and-white photographs]
-
A.
numberOfFiguresDepicted
Indicates the total count of distinct figures shown within a given depiction or representation.
-
B.
estimatedNumberOfPaintings
Indicates the approximate count of paintings associated with an entity, rather than an exact, verified number.
-
C.
numberOfVolumes
Indicates the total count of separate volumes or parts that make up a multi-volume work or collection.
-
D.
collectionSize
chosen
Indicates the total number of items contained within a specified collection.
-
E.
mirrorCount
Indicates the number of mirrors associated with or present in relation to a given entity or context.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a2e7f1d1bc81909cf2dc9754a3c334 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eebde1d881908fb212bfba9d7c67 |
completed | Feb. 28, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69a2edd3b948819097d96c73d0a0f699 |
completed | Feb. 28, 2026, 1:29 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.