Triple
T10444929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olivia Wenscombe |
E246261
|
entity |
| Predicate | relationshipTypeWithRobertAngier |
P94072
|
FINISHED |
| Object | romantic relationship |
—
|
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: romantic relationship | Statement: [Olivia Wenscombe, relationshipTypeWithRobertAngier, romantic relationship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithRobertAngier Context triple: [Olivia Wenscombe, relationshipTypeWithRobertAngier, romantic relationship]
-
A.
relationshipToIsabelArcher
Indicates the specific personal or social connection that an entity has to Isabel Archer.
-
B.
relationshipTypeWith Alicia Johns
Indicates the specific type or nature of the relationship that an entity has with Alicia Johns.
-
C.
relationshipTypeWithNinaSayers
Indicates the specific nature or category of relationship that an entity has with Nina Sayers.
-
D.
relationshipTypeWithJanieCrawford
Indicates the specific nature or category of relationship that an entity has with Janie Crawford.
-
E.
relationshipToCatherine
Indicates the specific familial, social, or interpersonal connection that one entity has to the person named Catherine.
- 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_69d381c04fe08190957c26c526a3b05a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4fe083cd881909d2d8ad75d1d94cb |
completed | April 7, 2026, 12:52 p.m. |
| PD | Predicate disambiguation | batch_69d4fb73a5e48190a8df4775bc5da80f |
completed | April 7, 2026, 12:41 p.m. |
| PDg | Predicate description generation | batch_69d4fe058fcc81909428137d9ffd6d90 |
completed | April 7, 2026, 12:52 p.m. |
Created at: April 6, 2026, 12:16 p.m.