Triple
T33336433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sombertown |
E853548
|
entity |
| Predicate | hasVillainousAuthorityFigure |
P188914
|
FINISHED |
| Object | Burgermeister Meisterburger |
—
|
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: Burgermeister Meisterburger | Statement: [Sombertown, hasVillainousAuthorityFigure, Burgermeister Meisterburger]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVillainousAuthorityFigure Context triple: [Sombertown, hasVillainousAuthorityFigure, Burgermeister Meisterburger]
-
A.
hasVillain
Indicates that one entity is the villain or primary antagonist associated with another entity.
-
B.
isAuthorityFigureFor
Indicates that one entity holds a position of recognized power, control, or formal responsibility over another entity.
-
C.
isTypeOfVillain
Indicates that one entity is classified as a particular type or category of villain in relation to another entity.
-
D.
hasOppressorInFiction
chosen
Indicates that, within a fictional context, one entity serves as the oppressor of another.
-
E.
hasNotableAuthorityFigure
Indicates that an entity is associated with a prominent individual who holds significant authority or leadership over it.
- 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_69f34969614c81909cd99661b0902533 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fd09840ea88190a2e6d7e577ade717 |
completed | May 7, 2026, 9:52 p.m. |
| PD | Predicate disambiguation | batch_69fd064c49988190afadddbd04d7cb94 |
completed | May 7, 2026, 9:38 p.m. |
Created at: May 1, 2026, 1:34 a.m.