Triple
T10721528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Annie Hall (character) |
E252832
|
entity |
| Predicate | wardrobeInfluenceOn |
P42256
|
FINISHED |
| Object | 1970s women’s fashion |
—
|
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: 1970s women’s fashion | Statement: [Annie Hall (character), wardrobeInfluenceOn, 1970s women’s fashion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wardrobeInfluenceOn Context triple: [Annie Hall (character), wardrobeInfluenceOn, 1970s women’s fashion]
-
A.
wardrobeFeature
Indicates that a wardrobe possesses or includes a specific feature, attribute, or functional element.
-
B.
fashionStyle
chosen
Indicates the characteristic way in which an entity dresses or presents themselves in terms of clothing and appearance.
-
C.
styleTendsTo
Indicates that one style is generally inclined or likely to develop, appear, or be adopted in the direction of another style.
-
D.
usesDressing
Indicates that one entity applies or employs a particular dressing (such as a sauce, covering, or treatment) in relation to another entity or context.
-
E.
styleCategory
Indicates the stylistic classification or genre category that an item, work, or entity belongs to.
- 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_69d6aa5d8be481909a43218b2bfdbe95 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d70d43655081909b071100c96cb4f6 |
completed | April 9, 2026, 2:21 a.m. |
| PD | Predicate disambiguation | batch_69d6f30455888190b77f476b8418eaee |
completed | April 9, 2026, 12:29 a.m. |
Created at: April 8, 2026, 9:13 p.m.