Triple
T26385165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harper Stewart |
E663256
|
entity |
| Predicate | relationshipStatusInTheBestMan |
P196487
|
FINISHED |
| Object | dating Robyn |
—
|
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: dating Robyn | Statement: [Harper Stewart, relationshipStatusInTheBestMan, dating Robyn]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipStatusInTheBestMan Context triple: [Harper Stewart, relationshipStatusInTheBestMan, dating Robyn]
-
A.
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.
-
B.
familyRelationshipStatus
Indicates the type and state of a familial relationship that exists between two entities.
-
C.
stateOfRelations
Indicates the current nature or condition of the relationship between two or more entities, such as whether it is friendly, hostile, neutral, or otherwise characterized.
-
D.
relationshipStatusWithMarnie
Indicates the nature or current state of the relationship that an entity has with Marnie.
-
E.
needsBestMan
Indicates that one entity requires or is seeking another entity to serve as their best man in a wedding or similar ceremony.
- 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_69ee88374adc81909868f3bab374a32f |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69fe59d11e9881909d2f33b7c717030e |
completed | May 8, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69fe394fdfbc8190a931926ae3635cbf |
completed | May 8, 2026, 7:28 p.m. |
| PDg | Predicate description generation | batch_69fe59d03a648190bbe846cb5730a477 |
completed | May 8, 2026, 9:46 p.m. |
Created at: April 26, 2026, 11:21 p.m.