Triple
T29434872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Bank |
E746541
|
entity |
| Predicate | antagonistLocationFor |
P167967
|
FINISHED |
| Object | Danny Ocean and his crew |
—
|
NE NERFINISHED |
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: Danny Ocean and his crew | Statement: [The Bank, antagonistLocationFor, Danny Ocean and his crew]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: antagonistLocationFor Context triple: [The Bank, antagonistLocationFor, Danny Ocean and his crew]
-
A.
antagonistBaseOf
Indicates that one entity serves as the primary base, headquarters, or stronghold from which an antagonist operates or exerts influence over another entity.
-
B.
antagonistNearby
Indicates that an opposing or hostile entity is located in close physical proximity to the reference entity.
-
C.
hasVillainBaseLocation
Indicates that a villain’s primary base or headquarters is located at a specified place.
-
D.
antagonistOf
Indicates a relationship where one entity actively opposes, conflicts with, or serves as an adversary to another.
-
E.
antagonistOrigin
Indicates the source, background, or cause from which an antagonist or opposing force arises in relation to another entity or narrative.
- 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_69f0a7a180e48190ae775e40047dbcb5 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69f66e5f7e30819094530abceabd5f43 |
completed | May 2, 2026, 9:36 p.m. |
| PD | Predicate disambiguation | batch_69f66abfdaf08190a55f14c70be6fd4d |
completed | May 2, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69f66d75a8788190aa9ca2c977429045 |
completed | May 2, 2026, 9:32 p.m. |
Created at: April 28, 2026, 3:15 p.m.