Triple
T8736397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bettie |
E207394
|
entity |
| Predicate | relatedName |
P3889
|
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: [Bettie, relatedName, Bette]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bette Context triple: [Bettie, relatedName, 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 the familiar nickname of Betty Ford, the former First Lady of the United States and founder of the Betty Ford Center for substance abuse treatment.
-
D.
Betty
Betty is a feminine given name, often a diminutive of Elizabeth, that has been widely used in English-speaking countries.
-
E.
Betty
Betty is the birth name of iconic American actress Lauren Bacall, a legendary figure of Hollywood's Golden Age.
- 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_69ca835a03a081909d4d4cd01a18c9fb |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d44275881909f7eb40b24180294 |
completed | March 31, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf517c6fac8190b782c8f441635814 |
completed | April 3, 2026, 5:34 a.m. |
Created at: March 30, 2026, 6:38 p.m.