Triple
T14356390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiki van Eijk |
E355979
|
entity |
| Predicate | hasWorkedFor |
P11675
|
FINISHED |
| Object | Hermès |
E135390
|
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: Hermès | Statement: [Kiki van Eijk, hasWorkedFor, Hermès]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hermès Context triple: [Kiki van Eijk, hasWorkedFor, Hermès]
-
A.
Hermès International
chosen
Hermès International is a French luxury goods manufacturer renowned for its high-end leather goods, fashion accessories, and ready-to-wear collections.
-
B.
Berluti
Berluti is a French luxury brand renowned for its high-end men's shoes, leather goods, and bespoke tailoring.
-
C.
Boucheron
Boucheron is a historic French luxury jewelry and watchmaking house renowned for its high-end, artistic designs and craftsmanship.
-
D.
Kering
Kering is a French multinational luxury group that owns and manages high-end fashion and leather goods brands such as Gucci, Saint Laurent, and Bottega Veneta.
-
E.
Chaumet
Chaumet is a historic French high jewelry and watchmaking house renowned for its exquisite tiaras and fine craftsmanship.
- 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_69d82790a7e08190877e2d349b2e8d8e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8f519bf881908615f4d47e0f77aa |
completed | April 14, 2026, 7:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c473fa48190866ab946971e971c |
completed | May 8, 2026, 2:36 a.m. |
Created at: April 10, 2026, 1:15 a.m.