Triple
T10225839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bebe Buell |
E243200
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Bebe Buell |
E243200
|
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: Bebe Buell | Statement: [Bebe Buell, name, Bebe Buell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bebe Buell Context triple: [Bebe Buell, name, Bebe Buell]
-
A.
Bebe Buell
chosen
Bebe Buell is an American model and singer known for her work as a 1970s fashion icon and for her high-profile relationships in the rock music scene.
-
B.
Bebe
Bebe is a contemporary women's fashion brand known for its trendy, body-conscious clothing and accessories.
-
C.
Bebe
Bebe is the nickname of Mary “Bebe” Hunt Kemper, a woman known primarily in relation to the Kemper family.
-
D.
Mackenzie Phillips
Mackenzie Phillips is an American actress and singer best known for her roles in the film "American Graffiti" and the sitcom "One Day at a Time."
-
E.
Mary “Bebe” Hunt Kemper
Mary “Bebe” Hunt Kemper is an American arts patron and philanthropist best known for establishing the Kemper Museum of Contemporary Art in Kansas City, Missouri.
- 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_69d381b0f97c819085c9b45799a5fb7c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d1f9cf6c81909a6b9e9b9d0a79fe |
completed | April 7, 2026, 9:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71c8511008190a30008ed32a983d1 |
completed | April 9, 2026, 3:27 a.m. |
Created at: April 6, 2026, 11:17 a.m.