Triple
T12987221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Byra Whittlesey |
E321799
|
entity |
| Predicate | hasSpouseNumber |
P58368
|
FINISHED |
| Object | first wife of Jack Hemingway |
—
|
LITERAL 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: first wife of Jack Hemingway | Statement: [Byra Whittlesey, hasSpouseNumber, first wife of Jack Hemingway]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpouseNumber Context triple: [Byra Whittlesey, hasSpouseNumber, first wife of Jack Hemingway]
-
A.
spouseCount
chosen
Indicates the number of spouses an entity has.
-
B.
spouseInFamily
Indicates that a person is a spouse (married partner) within the context of a specific family unit.
-
C.
coSpouse
Indicates that two individuals are married to each other as spouses.
-
D.
spouse name
Indicates that one entity is the legally recognized husband or wife of the other, specifying the partner’s name in a marital relationship.
-
E.
spouseOfHead
Indicates that one person is the married partner of the individual who holds the position of head (e.g., head of a household, organization, or state).
- 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69d97dbdd94c8190ac4bbecca02dc77b |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 8:41 p.m.