Triple
T12451759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UL |
E297548
|
entity |
| Predicate | underlyingCompanyOwnsBrand |
P6745
|
FINISHED |
| Object | Rexona |
E297552
|
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: Rexona | Statement: [UL, underlyingCompanyOwnsBrand, Rexona]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rexona Context triple: [UL, underlyingCompanyOwnsBrand, Rexona]
-
A.
Rexona
chosen
Rexona is a global deodorant and antiperspirant brand known for its long-lasting sweat and odor protection products.
-
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.
Febreze
Febreze is a popular household odor-eliminating product line known for its air fresheners and fabric refreshers.
-
D.
Garnier
Garnier is a French surname most famously associated with architect Charles Garnier, designer of the Paris Opéra.
-
E.
Harpic
Harpic is a widely used brand of toilet and bathroom cleaning products known for its powerful disinfecting and stain-removal formulations.
- 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_69d6ada166c48190b902972cd2408fa3 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d9541ace208190a5149b6f18fa196d |
completed | April 10, 2026, 7:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64b9f4dd08190b1d62b03d68cc8a6 |
completed | May 2, 2026, 7:08 p.m. |
Created at: April 8, 2026, 9:56 p.m.