Triple
T23444390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Caine as John Tarrant |
E565493
|
entity |
| Predicate | hasFamilyRelationInPlot |
P38221
|
FINISHED |
| Object | fatherOfKidnappedSon |
—
|
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: fatherOfKidnappedSon | Statement: [Michael Caine as John Tarrant, hasFamilyRelationInPlot, fatherOfKidnappedSon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFamilyRelationInPlot Context triple: [Michael Caine as John Tarrant, hasFamilyRelationInPlot, fatherOfKidnappedSon]
-
A.
hasFamilialTieTo
Indicates a relationship where two entities are connected by family bonds, such as by blood, marriage, or adoption.
-
B.
hasProtagonistFamilyMember
Indicates that a work’s protagonist has a specified individual as a member of their family.
-
C.
hasFamilyRelationContext
Indicates that there exists a family-based relationship or kinship context connecting the referenced entities.
-
D.
hasFamilyRole
chosen
Indicates that one entity holds a specific familial role or position in relation to another entity.
-
E.
belongsToFamily
Indicates that an entity is a member of, or is associated as part of, a specific family group.
- 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_69e24584f9488190bb32730bd2ce023e |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a64717d08190a2c25e7bbfc17a2f |
completed | April 29, 2026, 6:33 a.m. |
| PD | Predicate disambiguation | batch_69f061f92da081908e7f1d0cd1e9b01c |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 5:51 p.m.