Triple
T35403598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Luis, Capul |
E1023302
|
entity |
| Predicate | hasBarangayCaptain |
P193964
|
FINISHED |
| Object | barangay captain of San Luis, Capul |
—
|
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: barangay captain of San Luis, Capul | Statement: [San Luis, Capul, hasBarangayCaptain, barangay captain of San Luis, Capul]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBarangayCaptain Context triple: [San Luis, Capul, hasBarangayCaptain, barangay captain of San Luis, Capul]
-
A.
hasComponentBarangays
Indicates that an entity (typically a municipality, city, or similar administrative unit) is composed of or includes specific barangays as its subunits.
-
B.
isUrbanBarangay
Indicates that a given barangay is classified as urban rather than rural.
-
C.
hasNumberOfBarangays
Indicates the total count of barangays associated with a given administrative unit or locality.
-
D.
hasUrbanBarangays
Indicates that a place or administrative unit possesses one or more barangays classified as urban.
-
E.
barangay
Indicates that an entity is associated with, located in, or falls under the jurisdiction of a specific barangay (the smallest local administrative division).
- 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_69f76df43ca4819098711ca4370f1bb9 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fd5bf69acc819092a01e4259785dc3 |
completed | May 8, 2026, 3:43 a.m. |
| PD | Predicate disambiguation | batch_69fd59b3f4ac8190a7f9dd3142da6e09 |
completed | May 8, 2026, 3:34 a.m. |
| PDg | Predicate description generation | batch_69fd5bf49288819098a12202411cba4f |
completed | May 8, 2026, 3:43 a.m. |
Created at: May 3, 2026, 4:03 p.m.