Triple
T24820579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Municipal President of Badiraguato |
E621049
|
entity |
| Predicate | officeNameInOfficialLanguage |
P159039
|
FINISHED |
| Object | Presidente Municipal de Badiraguato |
—
|
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: Presidente Municipal de Badiraguato | Statement: [Municipal President of Badiraguato, officeNameInOfficialLanguage, Presidente Municipal de Badiraguato]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeNameInOfficialLanguage Context triple: [Municipal President of Badiraguato, officeNameInOfficialLanguage, Presidente Municipal de Badiraguato]
-
A.
officeNameInFrench
Indicates the French-language name or label used for a particular office or official position.
-
B.
officeNameInPortuguese
Indicates the name or title of an office as expressed in the Portuguese language.
-
C.
officeNameInDutch
Indicates the Dutch-language name or title used for a particular office, position, or role.
-
D.
hasLanguageOfOfficialName
Indicates that an entity’s official name is expressed in a specified language.
-
E.
officeNameInNynorsk
Indicates the name of an office expressed in the Nynorsk written standard of the Norwegian language.
- F. None of above. chosen
Provenance (4 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_69e2fabfd4648190bd0e5c7f4dbb6cab |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f497bc12b881908fe3386c66252bf6 |
completed | May 1, 2026, 12:08 p.m. |
| PD | Predicate disambiguation | batch_69f49366e8d08190adb4b71fe3a14683 |
completed | May 1, 2026, 11:49 a.m. |
| PDg | Predicate description generation | batch_69f497b8abb88190bb672cf6907c4b8d |
completed | May 1, 2026, 12:08 p.m. |
Created at: April 18, 2026, 5:04 a.m.