Triple
T18085641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anya Hindmarch |
E432826
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Anya Hindmarch |
—
|
NE NERFINISHED |
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: Anya Hindmarch | Statement: [Anya Hindmarch, name, Anya Hindmarch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anya Hindmarch Context triple: [Anya Hindmarch, name, Anya Hindmarch]
-
A.
Anya Hindmarch
chosen
Anya Hindmarch is a British fashion accessories designer renowned for her luxury handbags, playful designs, and influential collaborations in contemporary fashion.
-
B.
Lea McCartney
Lea McCartney is known as the sister of American singer and actor Jesse McCartney.
-
C.
Vivienne Kensington
Vivienne Kensington is a driven and initially antagonistic Harvard Law student who becomes a key supporting character and eventual ally to Elle Woods in the Broadway musical "Legally Blonde."
-
D.
Jill Stuart
Jill Stuart is an American fashion designer known for her contemporary, feminine clothing and accessories label popular on international runways.
-
E.
Stella McCartney
Stella McCartney is a British fashion designer renowned for her sustainable, animal-free luxury clothing and accessories.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b907d05c819083cc3bd6021089e6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4d9fdb00c8190b4769699e94c8941 |
completed | April 19, 2026, 1:34 p.m. |
Created at: April 10, 2026, 10:27 a.m.