Triple
T8385363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bennett Holiday |
E197804
|
entity |
| Predicate | associatedWithFictionalOrganization |
P10687
|
FINISHED |
| Object | Connex-Killen merger |
—
|
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: Connex-Killen merger | Statement: [Bennett Holiday, associatedWithFictionalOrganization, Connex-Killen merger]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithFictionalOrganization Context triple: [Bennett Holiday, associatedWithFictionalOrganization, Connex-Killen merger]
-
A.
setInFictionalOrganization
chosen
Indicates that an entity is located within, associated with, or takes place inside a fictional organization.
-
B.
ownedByFictionalCharacter
Indicates that something is possessed or owned by a fictional (not real-world) character.
-
C.
hasFictionalWork
Indicates that one entity is the creator, owner, or source of a fictional work associated with another entity.
-
D.
possibleAffiliation
Indicates that there is a potential or uncertain organizational, group, or institutional association between the entities, but the affiliation is not confirmed.
-
E.
hasFictionalStaffMember
Indicates that an entity includes or employs a staff member who is a fictional character.
- 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_69ca82f749388190bffbea6dfb509016 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb80e03eb08190a458c9caa0524e0f |
completed | March 31, 2026, 8:08 a.m. |
| PD | Predicate disambiguation | batch_69cb70cfe82881909fe374ba52649e84 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:02 p.m.