Triple
T12971101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bat Masterson |
E321396
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Emma Walter |
E321396
|
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: Emma Walter | Statement: [Bat Masterson, spouse, Emma Walter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emma Walter Context triple: [Bat Masterson, spouse, Emma Walter]
-
A.
Emma Walter
chosen
Emma Walter was the wife of famed Old West lawman, gambler, and sportswriter Bat Masterson.
-
B.
Ellen Walsh
Ellen Walsh is a personal name shared by multiple individuals, including professionals and public figures in various fields.
-
C.
Grace Nelson
Grace Nelson is an American political figure and philanthropist best known as the wife of former U.S. Senator and NASA Administrator Bill Nelson.
-
D.
Kate Wollman
Kate Wollman was a philanthropist whose donation funded the construction of the famous Wollman Rink in New York City's Central Park.
-
E.
Melinda Welles
Melinda Welles is a central character in the musical "On a Clear Day You Can See Forever," appearing as the elegant 18th-century past-life persona of the modern heroine.
- 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_69d80763bd6c819094437da5b20b01d2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e418d548190be1c73db76cb3aa8 |
completed | April 10, 2026, 10:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c6f404888190b7bb47bff1a7c1e1 |
completed | May 3, 2026, 10:06 p.m. |
Created at: April 9, 2026, 8:35 p.m.