Triple
T2094695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Eckford |
E32752
|
entity |
| Predicate | hasPhoto |
P12849
|
FINISHED |
| Object | famous 1957 photograph being harassed by a white mob |
—
|
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: famous 1957 photograph being harassed by a white mob | Statement: [Elizabeth Eckford, hasPhoto, famous 1957 photograph being harassed by a white mob]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPhoto Context triple: [Elizabeth Eckford, hasPhoto, famous 1957 photograph being harassed by a white mob]
-
A.
hasPhotograph
chosen
Indicates that one entity possesses, includes, or is associated with a photograph depicting or representing another entity.
-
B.
hasPortrait
Indicates that one entity possesses, displays, or is associated with a portrait depicting another entity.
-
C.
hasAIPhotoFeatures
Indicates that an entity provides or supports photo-related features powered by artificial intelligence.
-
D.
hasGallery
Indicates that one entity possesses, contains, or is associated with a gallery, such as a collection or display space.
-
E.
hasFilmPoster
Indicates that one entity serves as the film poster associated with another film entity.
- 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_69a885eba0708190999696a45cbec816 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abba99ddc48190bb2097b56efb7aca |
completed | March 7, 2026, 5:41 a.m. |
| PD | Predicate disambiguation | batch_69abb7b6274081909df36cd7a7c6a675 |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:43 p.m.