Triple
T13970256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lavezares |
E336041
|
entity |
| Predicate | hasBarangayType |
P102583
|
FINISHED |
| Object | coastal barangays |
—
|
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: coastal barangays | Statement: [Lavezares, hasBarangayType, coastal barangays]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBarangayType Context triple: [Lavezares, hasBarangayType, coastal barangays]
-
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.
hasUrbanBarangays
chosen
Indicates that a place or administrative unit possesses one or more barangays classified as urban.
-
C.
hasIslandBarangays
Indicates that a locality or administrative unit possesses barangays that are located on islands.
-
D.
hasNumberOfBarangays
Indicates the total count of barangays associated with a given administrative unit or locality.
-
E.
hasMunicipalityType
Indicates that an administrative unit is classified as having a specific type or category of municipality (e.g., city, town, village).
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e8daeac8190aadd4b3b60222482 |
completed | April 14, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69dd465a21408190b912a42c50ffa0d9 |
completed | April 13, 2026, 7:39 p.m. |
Created at: April 9, 2026, 10:18 p.m.