Triple
T14116864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Febreze |
E339795
|
entity |
| Predicate | hasBrandExtension |
P20317
|
FINISHED |
| Object | Febreze Car |
E339795
|
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: Febreze Car | Statement: [Febreze, hasBrandExtension, Febreze Car]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Febreze Car Context triple: [Febreze, hasBrandExtension, Febreze Car]
-
A.
Febreze
chosen
Febreze is a popular household odor-eliminating product line known for its air fresheners and fabric refreshers.
-
B.
Air Wick
Air Wick is a popular brand of air fresheners and home fragrance products known for its sprays, plug-ins, candles, and diffusers.
-
C.
Purola
Purola is a small town in the Uttarkashi district of Uttarakhand, India, known as a local commercial hub and gateway to nearby Himalayan regions.
-
D.
Rexona
Rexona is a global deodorant and antiperspirant brand known for its long-lasting sweat and odor protection products.
-
E.
Fabro
Fabro is a small Italian town in the Umbria region known for its medieval historic center and hilltop setting.
- 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_69d81c6a95b481909e39111e0c1f31ee |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de6010a03c81909f5f160f8d1fa8fa |
completed | April 14, 2026, 3:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcd0baa328819099511dfa7b9666d3 |
completed | May 7, 2026, 5:49 p.m. |
Created at: April 9, 2026, 10:22 p.m.