Triple
T3365872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Talia Shire |
E70833
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | David Shire |
E166298
|
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: David Shire | Statement: [Talia Shire, spouse, David Shire]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Shire Context triple: [Talia Shire, spouse, David Shire]
-
A.
David Shire
chosen
David Shire is an American composer best known for his film and television scores, including acclaimed work in the 1970s.
-
B.
Richard Shepherd
Richard Shepherd was an American film producer best known for his work on classic movies such as "Breakfast at Tiffany's."
-
C.
Ron Goodwin
Ron Goodwin was a British composer and conductor best known for his rousing film scores for war and adventure movies in the mid-20th century.
-
D.
Robert Mann
Robert Mann was a 19th-century American man best known as the son of influential education reformer Horace Mann.
-
E.
Michael Kamen
Michael Kamen was an American composer and conductor renowned for his film and television scores, including major works in action cinema and acclaimed historical dramas.
- 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_69ad85a729d48190afd789cd8417f289 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb287a30c8190b4c40091675c94fb |
completed | March 8, 2026, 5:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b34bbdebb88190be8458f840e2d84f |
completed | March 12, 2026, 11:26 p.m. |
Created at: March 8, 2026, 3:13 p.m.