Triple
T11559782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dramatically Different Moisturizing Lotion |
E274110
|
entity |
| Predicate | availability |
P7415
|
FINISHED |
| Object | Clinique counters |
E52784
|
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: Clinique counters | Statement: [Dramatically Different Moisturizing Lotion, availability, Clinique counters]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Clinique counters Context triple: [Dramatically Different Moisturizing Lotion, availability, Clinique counters]
-
A.
Clinique
chosen
Clinique is an American skincare and cosmetics brand known for its dermatologist-developed, fragrance-free products and clinical approach to beauty.
-
B.
Crest
Crest is a well-known oral care brand, particularly recognized for its toothpastes and whitening products.
-
C.
Crest
Crest is a historic town in southeastern France’s Drôme department, best known for its medieval tower, one of the tallest castle keeps in Europe.
-
D.
Garnier
Garnier is a French surname most famously associated with architect Charles Garnier, designer of the Paris Opéra.
-
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_69d6aae4dfa48190a3ab0b19a159a3c5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d88a899d4481909a3bce3147763b51 |
completed | April 10, 2026, 5:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e6e88b84d48190948243646bb5fd2b |
completed | April 21, 2026, 3:01 a.m. |
Created at: April 8, 2026, 9:37 p.m.