Triple
T8668957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marc Jacobs |
E205746
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Marc Jacobs Ready-to-Wear |
E750022
|
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: Marc Jacobs Ready-to-Wear | Statement: [Marc Jacobs, hasPart, Marc Jacobs Ready-to-Wear]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marc Jacobs Ready-to-Wear Context triple: [Marc Jacobs, hasPart, Marc Jacobs Ready-to-Wear]
-
A.
Marc Jacobs
Marc Jacobs is an influential American fashion designer known for his eponymous label and former role as creative director at Louis Vuitton.
-
B.
Marc Jacobs Beauty
Marc Jacobs Beauty is a cosmetics line associated with fashion designer Marc Jacobs, known for its stylish, high-end makeup products and distinctive packaging.
-
C.
Marc by Marc Jacobs
chosen
Marc by Marc Jacobs is a diffusion fashion line created by American designer Marc Jacobs, known for its youthful, accessible take on contemporary luxury style.
-
D.
Marchesa
Marchesa is the Italian noble title traditionally used to designate a woman holding the rank of marquess.
-
E.
Marchesa
Marchesa is a luxury fashion label renowned for its ornate, red-carpet-ready eveningwear and bridal gowns.
- 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_69ca83516ae88190aefe034b3bc589e3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc48a48b548190b78259072b1224ee |
completed | March 31, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cef3898bf88190959b361638d032de |
completed | April 2, 2026, 10:54 p.m. |
Created at: March 30, 2026, 6:31 p.m.