Triple
T29943179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jerry Warriner |
E760554
|
entity |
| Predicate | relationshipToLucyWarriner |
P203715
|
FINISHED |
| Object | estranged husband for part of the film |
—
|
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 husband for part of the film | Statement: [Jerry Warriner, relationshipToLucyWarriner, estranged husband for part of the film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToLucyWarriner Context triple: [Jerry Warriner, relationshipToLucyWarriner, estranged husband for part of the film]
-
A.
relationshipToLucyHoneychurch
Indicates the specific type of relationship or connection an entity has to Lucy Honeychurch.
-
B.
relationshipStatusWithLucyMoran
Indicates the type or state of the relationship that an entity has with Lucy Moran.
-
C.
relationshipToWinston
Indicates the specific type of personal, social, or familial connection that one entity has with Winston.
-
D.
relationshipTypeWith Jerry Warriner
Indicates the specific nature or category of the relationship that an entity has with Jerry Warriner.
-
E.
relationshipToLaureyWilliams
Indicates the nature or type of relational connection an entity has specifically to Laurey Williams.
- 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_69f22463f3648190a603c3ff305c660b |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_6a01d077dcec8190b05b24b5de313b15 |
completed | May 11, 2026, 12:50 p.m. |
| PD | Predicate disambiguation | batch_6a01cea0e37881909cb6888518c6d12f |
completed | May 11, 2026, 12:42 p.m. |
| PDg | Predicate description generation | batch_6a01d0771fb88190809ac7ba18fb6565 |
completed | May 11, 2026, 12:49 p.m. |
Created at: April 29, 2026, 6:23 p.m.