Triple
T9477132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Petalburg |
E228542
|
entity |
| Predicate | governedBy |
P46
|
FINISHED |
| Object | Mayor Kroop |
E802013
|
NE 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 Kroop | Statement: [Petalburg, governedBy, Mayor Kroop]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mayor Kroop Context triple: [Petalburg, governedBy, Mayor Kroop]
-
A.
Mayor Kroop
chosen
Mayor Kroop is a fictional character who serves as the mayor of Petalburg in the Pokémon universe.
-
B.
Mayor McGerkle
Mayor McGerkle is a cheerful, well-meaning civic leader in the 2018 animated film "The Grinch," serving as the enthusiastic mayor of Whoville.
-
C.
Mayor Tom Kane
Mayor Tom Kane is the ruthless, politically powerful fictional mayor of Chicago portrayed by Kelsey Grammer in the television series "Boss."
-
D.
Mayor Harla Branno
Mayor Harla Branno is a powerful political leader of the First Foundation in Isaac Asimov's science fiction universe, known for her shrewd, pragmatic governance and central role in the novel "Foundation's Edge."
-
E.
Mayor Gil Corrigan
Mayor Gil Corrigan is a fictional small-town mayor featured as a supporting character in the 1955 Western film "Top Gun."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca847162c48190b079076c9595513c |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd801386948190890133f622ff360b |
completed | April 1, 2026, 8:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d139f261248190b8e3238f7618191d |
completed | April 4, 2026, 4:18 p.m. |
Created at: March 30, 2026, 7:54 p.m.