Triple
T7049491
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How to Steal a Million |
E163727
|
entity |
| Predicate | costumeDesigner |
P184
|
FINISHED |
| Object | Givenchy |
E57810
|
NE 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: Givenchy | Statement: [How to Steal a Million, costumeDesigner, Givenchy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Givenchy Context triple: [How to Steal a Million, costumeDesigner, Givenchy]
-
A.
Givenchy
chosen
Givenchy is a renowned French luxury fashion and perfume house known for its haute couture, ready-to-wear collections, and iconic collaborations with celebrities and models.
-
B.
Chanel
Chanel is a legendary French luxury fashion house renowned for its haute couture, ready-to-wear, handbags, fragrances, and timeless designs such as the Chanel No. 5 perfume and the classic tweed suit.
-
C.
Chloé
Chloé is a French luxury fashion house renowned for its feminine ready-to-wear, leather goods, and accessories.
-
D.
Kenzo
Kenzo is a Japanese masculine given name borne by various notable figures in fields such as architecture, fashion, and entertainment.
-
E.
Nina Ricci
Nina Ricci is a French luxury fashion house renowned for its elegant haute couture, ready-to-wear collections, and iconic fragrances.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c6885f598c8190b6b6495c59d8d962 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e24d5e8c8190b37e56107e6da8ab |
completed | March 27, 2026, 8:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7888bba9c8190b6414b56e5588ec0 |
completed | March 28, 2026, 7:51 a.m. |
Created at: March 27, 2026, 2:37 p.m.