Triple
T7850313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorothy Auerbach |
E182031
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Dorothy Auerbach |
E182031
|
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: Dorothy Auerbach | Statement: [Dorothy Auerbach, name, Dorothy Auerbach]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dorothy Auerbach Context triple: [Dorothy Auerbach, name, Dorothy Auerbach]
-
A.
Dorothy Auerbach
chosen
Dorothy Auerbach was the wife of legendary Boston Celtics coach and executive Red Auerbach.
-
B.
Roberta Seidman
Roberta Seidman was the wife of American actor John Garfield, a prominent film star of the 1930s and 1940s.
-
C.
Judith Nelson
Judith Nelson was an American soprano known for her pioneering work and acclaimed performances in the early music and Baroque repertoire.
-
D.
Lila Vogel
Lila Vogel is the mother of journalist Lloyd Vogel, a character in the film "A Beautiful Day in the Neighborhood."
-
E.
June Preisser
June Preisser was an American film actress and dancer best known for her energetic supporting roles in 1930s and 1940s Hollywood musicals, often playing peppy, acrobatic teenagers.
- 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_69ca82869ee08190b8f9040dbc2c0467 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb18eaac508190bf373b1d50b52e1e |
completed | March 31, 2026, 12:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd66ecea6c819097a74513c5d84193 |
completed | April 1, 2026, 6:41 p.m. |
Created at: March 30, 2026, 4:50 p.m.