Triple
T13540532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bett |
E323370
|
entity |
| Predicate | derivedFrom |
P909
|
FINISHED |
| Object | Bette |
E60484
|
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: Bette | Statement: [Bett, derivedFrom, Bette]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bette Context triple: [Bett, derivedFrom, Bette]
-
A.
Bette
chosen
Bette is the given name of American singer, actress, and comedian Bette Midler.
-
B.
Bettie
Bettie is a feminine given name, often used as a diminutive or variant of names like Bettina or Elizabeth.
-
C.
Betty
Betty is a feminine given name, often a diminutive of Elizabeth, that has been widely used in English-speaking countries.
-
D.
Betty
"Betty" is the Allied reporting name for the Mitsubishi G4M, a Japanese World War II twin-engine land-based bomber known for its long range and vulnerability due to lack of armor and self-sealing fuel tanks.
-
E.
Betty
Betty is the troubled, passionate young woman at the center of the French cult film "Betty Blue," whose intense love affair and psychological unraveling drive the story.
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafd8ba10819098faadcc6adf251e |
completed | April 12, 2026, 2:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75d9eaff881909a3cd9e88bb4ec5e |
completed | May 3, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:45 p.m.