Triple
T25325368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bluestar Airlines |
E634996
|
entity |
| Predicate | conflictsWithInFiction |
P97499
|
FINISHED |
| Object | Gordon Gekko |
—
|
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: Gordon Gekko | Statement: [Bluestar Airlines, conflictsWithInFiction, Gordon Gekko]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: conflictsWithInFiction Context triple: [Bluestar Airlines, conflictsWithInFiction, Gordon Gekko]
-
A.
eraWithinFiction
Indicates that a time period or era exists inside the narrative world or timeline of a fictional work.
-
B.
worksInFictionalContext
Indicates that an entity performs work or fulfills a role within a fictional or imagined setting rather than in real-world circumstances.
-
C.
opposesFictional
chosen
Indicates that one fictional entity is in opposition or conflict with another within a narrative or imagined context.
-
D.
hasFictionalUniverseConflict
Indicates that there is a conflict or incompatibility between the fictional universes associated with the related entities.
-
E.
hasRelativeInFiction
Indicates that one entity has a relative or family member who appears as a character within a fictional work associated with the other entity.
- 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_69e75a9908108190a95427a97020632a |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f497bc12b881908fe3386c66252bf6 |
completed | May 1, 2026, 12:08 p.m. |
| PD | Predicate disambiguation | batch_69f45d06d0388190b36ecde92013624a |
completed | May 1, 2026, 7:57 a.m. |
Created at: April 21, 2026, 1:30 p.m.