Triple
T25575488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lyda Bunker |
E641094
|
entity |
| Predicate | hadSpouse |
P33561
|
FINISHED |
| Object | Haroldson Lafayette Hunt |
—
|
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: Haroldson Lafayette Hunt | Statement: [Lyda Bunker, hadSpouse, Haroldson Lafayette Hunt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadSpouse Context triple: [Lyda Bunker, hadSpouse, Haroldson Lafayette Hunt]
-
A.
hasNamesakeSpouse
Indicates that one entity has a spouse who shares the same name as another specified entity.
-
B.
spouseAssociatedWith
chosen
Indicates a marital or spousal relationship or close association between two entities.
-
C.
spouseOfType
Indicates that one entity is the spouse of another, specifying the type or role of that spousal relationship.
-
D.
spouseOfSince
Indicates that two individuals are spouses and specifies the date or time from which their marital relationship has been in effect.
-
E.
marriedBy
Indicates that one entity is the officiant or authority who performs and formalizes the marriage of another entity.
- 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_69e75dc281bc819095ec04dc0c3a94d0 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f92fb11c819086165e59ffef4910 |
completed | May 2, 2026, 1:16 p.m. |
| PD | Predicate disambiguation | batch_69f468421ba08190880eac99135e5970 |
completed | May 1, 2026, 8:45 a.m. |
Created at: April 21, 2026, 4:01 p.m.