Triple
T16608868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simone Tata |
E403513
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Lakmé |
E712582
|
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: Lakmé | Statement: [Simone Tata, employer, Lakmé]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lakmé Context triple: [Simone Tata, employer, Lakmé]
-
A.
Lakmé
chosen
Lakmé is a French opera by Léo Delibes, best known for its exotic setting in colonial India and its famous "Flower Duet."
-
B.
Neutrogena
Neutrogena is a widely recognized skincare and cosmetics brand known for its dermatologist-recommended products, including facial cleansers, moisturizers, sunscreens, and acne treatments.
-
C.
Lancôme
Lancôme is a French luxury cosmetics and skincare brand renowned for its high-end perfumes, makeup, and beauty products.
-
D.
Maybelline New York
Maybelline New York is a major American cosmetics and beauty brand known worldwide for its mass-market makeup products.
-
E.
Shiseido
Shiseido is a major Japanese multinational cosmetics and skincare company known for its high-end beauty products and long-standing global presence.
- 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_69d883880d0c81908b5fcd454e767b60 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e36094920881908051eb0a52e08440 |
completed | April 18, 2026, 10:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0075aa79408190b395d4cd9c6c1cb7 |
completed | May 10, 2026, 12:10 p.m. |
Created at: April 10, 2026, 5:17 a.m.