Triple
T4390410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betty White |
E99347
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Betty |
E352618
|
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: Betty | Statement: [Betty White, givenName, Betty]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Betty Context triple: [Betty White, givenName, Betty]
-
A.
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.
-
B.
Betty
chosen
Betty is a feminine given name, often a diminutive of Elizabeth, that has been widely used in English-speaking countries.
-
C.
Betty
Betty is the birth name of iconic American actress Lauren Bacall, a legendary figure of Hollywood's Golden Age.
-
D.
Betty
Betty is a minor character in Enid Blyton’s "Malory Towers" series, known as a lively and mischievous schoolgirl at the boarding school.
-
E.
Betty
"Betty" is a renowned photorealistic painting by German artist Gerhard Richter, depicting his daughter turning away from the viewer and exemplifying his exploration of perception and representation.
- 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_69b3454f739481909ff6c28331f0c0b9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35282cb2c8190856da20bb87e88ff |
completed | March 12, 2026, 11:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5e530428881908d125971263bd747 |
completed | March 14, 2026, 10:46 p.m. |
Created at: March 12, 2026, 11:19 p.m.