Triple
T17669249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Crabbe |
E440472
|
entity |
| Predicate | isFictionalSpouseOf |
P30304
|
FINISHED |
| Object | fictional police detective |
—
|
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: fictional police detective | Statement: [Margaret Crabbe, isFictionalSpouseOf, fictional police detective]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isFictionalSpouseOf Context triple: [Margaret Crabbe, isFictionalSpouseOf, fictional police detective]
-
A.
sometimesSpouseOf
Indicates that two entities are occasionally, but not consistently or permanently, in a spousal relationship with each other.
-
B.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
C.
isFianceeOf
Indicates that one person is the engaged-to-be-married partner of another person.
-
D.
hasSpouseInStory
chosen
Indicates that one entity is depicted as the spouse of another within the context of a particular story or narrative.
-
E.
hasNamesakeSpouse
Indicates that one entity has a spouse who shares the same name as another specified 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_69d8b9e87e18819087104a44dc4dc5b1 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46f67f6188190a978c7c9f462064d |
completed | April 19, 2026, 6 a.m. |
| PD | Predicate disambiguation | batch_69e3cde007d8819090dd92eea9f022cc |
completed | April 18, 2026, 6:30 p.m. |
Created at: April 10, 2026, 9:58 a.m.