Triple
T27596574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carter Heywood |
E699915
|
entity |
| Predicate | employerFictional |
P40735
|
FINISHED |
| Object | Mayor of New York City |
—
|
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: Mayor of New York City | Statement: [Carter Heywood, employerFictional, Mayor of New York City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employerFictional Context triple: [Carter Heywood, employerFictional, Mayor of New York City]
-
A.
employerInPlot
Indicates that one entity serves as the employer of another within the context of a specific plot or storyline.
-
B.
fictionalCorporation
Indicates that an entity is a corporation that exists only in fiction rather than in the real world.
-
C.
employerIn
Indicates that one entity serves as the employer of another within a specified context, such as a location, organization, or time period.
-
D.
employerInReality
Indicates that one entity is the actual, real-world employer of another entity, as opposed to a nominal, legal, or assumed employer.
-
E.
employerInUniverse
chosen
Indicates that one entity serves as the employer of another within a specified universe, context, or world.
- 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_69ef6a4d71f081909a1235763206b691 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f674e06c9481909ed0ea736408f0d7 |
completed | May 2, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69f673c2f81c8190bf369226306eef09 |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 27, 2026, 2:06 p.m.