Triple
T31154088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gotham University |
E794154
|
entity |
| Predicate | hasFictionalAffiliation |
P104242
|
FINISHED |
| Object | Gotham City |
—
|
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: Gotham City | Statement: [Gotham University, hasFictionalAffiliation, Gotham City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalAffiliation Context triple: [Gotham University, hasFictionalAffiliation, Gotham City]
-
A.
associatedWithFictionalGroup
chosen
Indicates that an entity has a connection or affiliation with a fictional group, organization, or collective.
-
B.
hasFictionalMember
Indicates that a group, organization, or collection includes at least one member that is fictional rather than real.
-
C.
isFictionalAgentOf
Indicates that one entity is a fictional character or agent that acts on behalf of, or represents, another entity.
-
D.
hasFictionalAlias
Indicates that an entity is known by an alternative name or identity within a fictional context.
-
E.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
- 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_69f224d41bb48190a5621cd1485e3a30 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fe610e1f6881908f10070ba64643cf |
completed | May 8, 2026, 10:17 p.m. |
| PD | Predicate disambiguation | batch_69fe604c6c008190ad659e9b9fa82f7b |
completed | May 8, 2026, 10:14 p.m. |
Created at: April 29, 2026, 9:06 p.m.