Triple
T1528859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helena Christensen |
E32395
|
entity |
| Predicate | workedFor |
P1910
|
FINISHED |
| Object | Revlon |
E52258
|
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: Revlon | Statement: [Helena Christensen, workedFor, Revlon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Revlon Context triple: [Helena Christensen, workedFor, Revlon]
-
A.
Revlon
chosen
Revlon is a major American cosmetics, skincare, fragrance, and personal care company known for its mass-market beauty products and global brand presence.
-
B.
Maybelline New York
Maybelline New York is a major American cosmetics and beauty brand known worldwide for its mass-market makeup products.
-
C.
Essie
Essie is a popular nail polish and nail care brand known for its wide range of fashion-forward colors and salon-quality formulas.
-
D.
NYX Professional Makeup
NYX Professional Makeup is a popular, affordable cosmetics brand known for its wide range of highly pigmented, trend-driven makeup products favored by both professionals and everyday consumers.
-
E.
L'Oréal
L'Oréal is a French multinational cosmetics and beauty company recognized as one of the world’s largest and most influential personal care brands.
- 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_69a885ea86308190998f6bc14bb91f8e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abb1dfd1a48190804ca5f0fb6f5985 |
completed | March 7, 2026, 5:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad2955dc788190988ebf911714437b |
completed | March 8, 2026, 7:46 a.m. |
Created at: March 4, 2026, 7:26 p.m.