Triple
T11036351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bill Haydon |
E260895
|
entity |
| Predicate | hasAffairWith |
P23617
|
FINISHED |
| Object | Ann Smiley |
E245051
|
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: Ann Smiley | Statement: [Bill Haydon, hasAffairWith, Ann Smiley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ann Smiley Context triple: [Bill Haydon, hasAffairWith, Ann Smiley]
-
A.
Ann Smiley
chosen
Ann Smiley is the unfaithful and enigmatic wife of British intelligence officer George Smiley in John le Carré’s spy novels.
-
B.
Virginia Weidler
Virginia Weidler was an American child actress of the 1930s and 1940s, best remembered for her witty supporting roles in classic Hollywood films such as "The Philadelphia Story."
-
C.
Ann Kirkpatrick
Ann Kirkpatrick is an American politician and attorney best known for serving multiple terms as a U.S. Representative from Arizona.
-
D.
Laura Deming
Laura Deming is a venture capitalist and longevity researcher best known for founding The Longevity Fund, which invests in companies developing therapies to extend healthy human lifespan.
-
E.
Erinn Bartlett
Erinn Bartlett is an American actress and former beauty pageant titleholder known for supporting roles in film and television.
- 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_69d6aa979bdc8190bf0e79104cc098c1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797e9e3fc8190802195ac9fcb8e28 |
completed | April 9, 2026, 12:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3a9b70074819084c725c5babf2fb7 |
completed | April 18, 2026, 3:56 p.m. |
Created at: April 8, 2026, 9:25 p.m.