Triple
T18002439
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lipa |
E430659
|
entity |
| Predicate | isInGeographicalCategory |
P116678
|
FINISHED |
| Object | Cities in Batangas |
—
|
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: Cities in Batangas | Statement: [Lipa, isInGeographicalCategory, Cities in Batangas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isInGeographicalCategory Context triple: [Lipa, isInGeographicalCategory, Cities in Batangas]
-
A.
containsGeographicalArea
Indicates that one geographical area spatially encompasses or includes another geographical area within its boundaries.
-
B.
hasGeographicType
chosen
Indicates that an entity is associated with or classified by a specific type or category of geographic feature or area.
-
C.
isGeographicalEntity
Indicates that something exists as a distinct geographic feature, area, or place within physical space.
-
D.
meetsInCountrySubdivision
Indicates that two or more entities meet or have a meeting within a specific administrative subdivision of a country (such as a state, province, or region).
-
E.
hasTypicalGeographicOrigin
Indicates that an entity is commonly or characteristically associated with originating from a particular geographic location.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b3e9498c8190bdfa7a53b0c0d8db |
completed | April 19, 2026, 10:52 a.m. |
| PD | Predicate disambiguation | batch_69e3f90039e4819080527f860dca042e |
completed | April 18, 2026, 9:34 p.m. |
Created at: April 10, 2026, 10:23 a.m.