Triple
T38393210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Esther |
E899782
|
entity |
| Predicate | makesClothingFor |
P178771
|
FINISHED |
| Object | Wealthy white women |
—
|
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: Wealthy white women | Statement: [Esther, makesClothingFor, Wealthy white women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: makesClothingFor Context triple: [Esther, makesClothingFor, Wealthy white women]
-
A.
designsClothesFor
chosen
Indicates that one entity creates or plans clothing specifically intended for another entity.
-
B.
designedCostumesFor
Indicates that one entity created or planned the costumes used by another entity, typically for a performance, production, or event.
-
C.
tailors
Indicates that one entity customizes, adapts, or modifies something specifically to suit the needs, characteristics, or preferences of another entity.
-
D.
showsClothing
Indicates that one entity visually presents or displays an item of clothing associated with another entity.
-
E.
hasGarment
Indicates that one entity possesses, wears, or is associated with a particular garment.
- 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_69f76e5c9b808190b486523f5c2f817d |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fcd1499e2c81909bafd84dc4810f45 |
completed | May 7, 2026, 5:52 p.m. |
| PD | Predicate disambiguation | batch_69fcccf024ec819086383ffbb6cfc036 |
completed | May 7, 2026, 5:33 p.m. |
Created at: May 3, 2026, 4:31 p.m.