Triple
T10721611
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vickie LaMotta |
E252835
|
entity |
| Predicate | relationshipTypeWith Jake LaMotta |
P95628
|
FINISHED |
| Object | turbulent marriage |
—
|
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: turbulent marriage | Statement: [Vickie LaMotta, relationshipTypeWith Jake LaMotta, turbulent marriage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWith Jake LaMotta Context triple: [Vickie LaMotta, relationshipTypeWith Jake LaMotta, turbulent marriage]
-
A.
relationshipToJackBrown
Indicates the specific familial, social, or professional relationship that an entity has to Jack Brown.
-
B.
relationshipTypeWith Francesca Johnson
Indicates the specific nature or category of the relationship that an entity has with Francesca Johnson.
-
C.
relationshipToJoeKeller
Indicates the specific familial, social, or personal connection that one entity has to Joe Keller.
-
D.
relationshipTypeWithSport
Indicates the specific type or nature of the relationship an entity has with a particular sport (e.g., participation, affiliation, or role).
-
E.
relationshipTypeWith Alicia Johns
Indicates the specific type or nature of the relationship that an entity has with Alicia Johns.
- 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_69d6aa5d8be481909a43218b2bfdbe95 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d70d43655081909b071100c96cb4f6 |
completed | April 9, 2026, 2:21 a.m. |
| PD | Predicate disambiguation | batch_69d6f30455888190b77f476b8418eaee |
completed | April 9, 2026, 12:29 a.m. |
| PDg | Predicate description generation | batch_69d6fa323564819097b207eb53f8a9b8 |
completed | April 9, 2026, 1 a.m. |
Created at: April 8, 2026, 9:13 p.m.