Triple
T15958791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Anderton |
E387003
|
entity |
| Predicate | familyStatusInFilm |
P8517
|
FINISHED |
| Object | divorced |
—
|
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: divorced | Statement: [John Anderton, familyStatusInFilm, divorced]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: familyStatusInFilm Context triple: [John Anderton, familyStatusInFilm, divorced]
-
A.
parentalStatusDuringFilm
Indicates the parental status or situation of a person at the time a film was made or released.
-
B.
familyAspect
chosen
Indicates a relationship where one entity is characterized by a particular familial role, status, or aspect in relation to another entity.
-
C.
familySocialStatus
Indicates the social standing or class position associated with a person’s family within a society.
-
D.
familyDepiction
Indicates that one entity visually represents or portrays members of a family or familial relationships.
-
E.
familyOf
Indicates a familial relationship exists between the entities, such as by blood, marriage, or adoption.
- 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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142d6fb588190b4176eab4bbae774 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:53 a.m.