Triple
T34824618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Talbot |
E1003883
|
entity |
| Predicate | marriedToActress |
P144544
|
FINISHED |
| Object | Gates McFadden |
—
|
NE NERFINISHED |
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: Gates McFadden | Statement: [John Talbot, marriedToActress, Gates McFadden]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToActress Context triple: [John Talbot, marriedToActress, Gates McFadden]
-
A.
marriedToBeforeFameOf
Indicates that one person was married to another person before the latter became famous.
-
B.
marriedToNotablePerson
Indicates that a person is legally married to another individual who is widely recognized or notable.
-
C.
hasSpouseActorsInLeads
Indicates that the primary leading roles in a work are performed by actors who are spouses of each other.
-
D.
marriedBy
Indicates that one entity is the officiant or authority who performs and formalizes the marriage of another entity.
-
E.
marriedToProfession
chosen
Indicates a relationship where a person is married to someone who has a specified profession or occupational role.
- F. None of above.
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_69f76db717088190811b4e744610f37d |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69fd7b0503a08190ba07338365b6fcc9 |
completed | May 8, 2026, 5:56 a.m. |
| PD | Predicate disambiguation | batch_69fd7a9733dc81909199f453c0cc2bc1 |
completed | May 8, 2026, 5:54 a.m. |
Created at: May 3, 2026, 4 p.m.