Triple
T35347162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tagudin |
E1020770
|
entity |
| Predicate | hasNeighboringAdministrativeUnitType |
P193775
|
FINISHED |
| Object | municipality |
—
|
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: municipality | Statement: [Tagudin, hasNeighboringAdministrativeUnitType, municipality]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNeighboringAdministrativeUnitType Context triple: [Tagudin, hasNeighboringAdministrativeUnitType, municipality]
-
A.
hasNeighbouringAdministrativeUnitType
chosen
Indicates that one administrative unit is directly adjacent to another administrative unit of a specified type.
-
B.
hasNeighbouringDivision
Indicates that one division is directly adjacent to and shares a boundary with another division.
-
C.
hasNeighboringLGA
Indicates that one local government area is geographically adjacent to or directly borders another local government area.
-
D.
hasNeighboringChiefdom
Indicates that one chiefdom is geographically adjacent to or directly borders another chiefdom.
-
E.
hasNeighbouringState
Indicates that one state shares a common border or is directly adjacent geographically to another state.
- 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_69f76decd95c8190ae428f6a19d535de |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a0010e46d948190a51111b5270fade7 |
completed | May 10, 2026, 5 a.m. |
| PD | Predicate disambiguation | batch_6a001061d34c8190bfe73f3d7c061eb7 |
completed | May 10, 2026, 4:58 a.m. |
Created at: May 3, 2026, 4:03 p.m.