Triple
T11061602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sinirangan Kabisay-an |
E261519
|
entity |
| Predicate | isAdministrativeRegion |
P84051
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Sinirangan Kabisay-an, isAdministrativeRegion, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isAdministrativeRegion Context triple: [Sinirangan Kabisay-an, isAdministrativeRegion, true]
-
A.
hasAdministrativeArea
Indicates that one entity serves as the governing or jurisdictional area responsible for administering another entity.
-
B.
isRegion
chosen
Indicates that one entity functions as a geographic or administrative region associated with another entity.
-
C.
representsAdministrativeRegion
Indicates that one entity serves as the administrative region or jurisdiction within which the other entity is governed or managed.
-
D.
associatedAdministrativeRegion
Indicates that an entity is linked to, or falls under the jurisdiction of, a particular administrative region.
-
E.
administrativeTerritoryType
Indicates the classification of an administrative area according to its level or type of territorial governance (e.g., city, county, province).
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d798ea834c819099401e69f995c59f |
completed | April 9, 2026, 12:17 p.m. |
| PD | Predicate disambiguation | batch_69d74411d9e881908c0eeafa0f38e4b6 |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:26 p.m.