Triple
T12268205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neutrogena Wing |
E292400
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Neutrogena |
E49849
|
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: Neutrogena | Statement: [Neutrogena Wing, namedAfter, Neutrogena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neutrogena Context triple: [Neutrogena Wing, namedAfter, Neutrogena]
-
A.
Neutrogena
chosen
Neutrogena is a widely recognized skincare and cosmetics brand known for its dermatologist-recommended products, including facial cleansers, moisturizers, sunscreens, and acne treatments.
-
B.
Nivea
Nivea is an American R&B singer best known for her early-2000s hits like "Don't Mess with My Man" and collaborations with prominent hip-hop artists.
-
C.
Neutrogena Wing
Neutrogena Wing is a dedicated gallery space within the Museum of International Folk Art that showcases a significant collection of global folk and traditional arts.
-
D.
Garnier
Garnier is a French surname most famously associated with architect Charles Garnier, designer of the Paris Opéra.
-
E.
Lakmé
Lakmé is a French opera by Léo Delibes, best known for its exotic setting in colonial India and its famous "Flower Duet."
- 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_69d6ab6856488190b5d31178d5015f8e |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cdd7b3c8190afd237cd9b633d4d |
completed | April 10, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e6799088190a5644267733ca2e5 |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:52 p.m.