Triple
T7822920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christopher Crawford |
E181174
|
entity |
| Predicate | hasRelationshipStatusWithJoanCrawford |
P79206
|
FINISHED |
| Object | estranged |
—
|
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: estranged | Statement: [Christopher Crawford, hasRelationshipStatusWithJoanCrawford, estranged]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipStatusWithJoanCrawford Context triple: [Christopher Crawford, hasRelationshipStatusWithJoanCrawford, estranged]
-
A.
relationshipToJanieCrawford
Indicates the specific type of personal or social relationship an entity has with Janie Crawford.
-
B.
relationshipTypeWithJanieCrawford
Indicates the specific nature or category of relationship that an entity has with Janie Crawford.
-
C.
marriageStatusWithMaryPickford
Indicates the marital relationship status that an entity has or had with Mary Pickford.
-
D.
marriedToArthurMillerBefore
Indicates that a person was married to Arthur Miller at some time prior to a specified reference point or event.
-
E.
relationshipStatusDuringFilm
Indicates the type or state of a relationship between entities specifically during the time period in which a film takes place or is produced.
- F. None of above. chosen
Provenance (4 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_69ca8282ccec819083c48efb72d21cf9 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cafa095d7081908b3e492ce58b5d5f |
completed | March 30, 2026, 10:32 p.m. |
| PD | Predicate disambiguation | batch_69cae91ae008819098e56bbe51143b31 |
completed | March 30, 2026, 9:20 p.m. |
| PDg | Predicate description generation | batch_69caf7855a3c81908b9318f7186fc0c0 |
completed | March 30, 2026, 10:21 p.m. |
Created at: March 30, 2026, 4:42 p.m.