Triple
T390487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Florence Nightingale Graham |
E8868
|
entity |
| Predicate | usedPseudonym |
P3799
|
FINISHED |
| Object | Elizabeth Arden |
E1396
|
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: Elizabeth Arden | Statement: [Florence Nightingale Graham, usedPseudonym, Elizabeth Arden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Arden Context triple: [Florence Nightingale Graham, usedPseudonym, Elizabeth Arden]
-
A.
Elizabeth Arden
chosen
Elizabeth Arden was a pioneering Canadian-American businesswoman who founded the Elizabeth Arden cosmetics empire and helped shape the modern beauty industry.
-
B.
Lancôme
Lancôme is a French luxury cosmetics and skincare brand renowned for its high-end perfumes, makeup, and beauty products.
-
C.
Yves Saint Laurent Beauté
Yves Saint Laurent Beauté is a luxury cosmetics and fragrance brand known for its high-end makeup, skincare, and iconic perfumes.
-
D.
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.
-
E.
Maybelline New York
Maybelline New York is a major American cosmetics and beauty brand known worldwide for its mass-market makeup products.
- 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec5d73e881909101308a583c8f13 |
completed | Feb. 28, 2026, 1:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a413f1148481908b4a25355ee07069 |
completed | March 1, 2026, 10:24 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.