Triple
T25189361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amber Waves |
E630820
|
entity |
| Predicate | professionalRelationshipWithJackHorner |
P125661
|
FINISHED |
| Object | collaborator in adult films |
—
|
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: collaborator in adult films | Statement: [Amber Waves, professionalRelationshipWithJackHorner, collaborator in adult films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionalRelationshipWithJackHorner Context triple: [Amber Waves, professionalRelationshipWithJackHorner, collaborator in adult films]
-
A.
relationshipToJack
chosen
Indicates the specific type of personal or social connection an entity has with Jack.
-
B.
relationshipToJackBrown
Indicates the specific familial, social, or professional relationship that an entity has to Jack Brown.
-
C.
relationshipToJerryLundegaard
Indicates the specific familial, social, or professional relationship that one entity has to Jerry Lundegaard.
-
D.
relationshipTypeWith J. C. Leyendecker
Indicates the specific type or nature of the relationship that an entity has with J. C. Leyendecker.
-
E.
hasRelationshipTypeWith Frank Drebin
Indicates that there exists a specific type of relationship between an entity and Frank Drebin.
- 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_69e75a8a6d088190ba1e82a4345225e7 |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f46e0c97e881909e2c3facd145014a |
completed | May 1, 2026, 9:10 a.m. |
| PD | Predicate disambiguation | batch_69f45cfb53f4819099bba48c5057e787 |
completed | May 1, 2026, 7:57 a.m. |
Created at: April 21, 2026, 12:44 p.m.