Triple
T12408075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zamboanga Sibugay |
E296438
|
entity |
| Predicate | hasComponentCityCount |
P101584
|
FINISHED |
| Object | 0 |
—
|
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: 0 | Statement: [Zamboanga Sibugay, hasComponentCityCount, 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasComponentCityCount Context triple: [Zamboanga Sibugay, hasComponentCityCount, 0]
-
A.
hasNumberOfComponentCities
chosen
Indicates the relationship that specifies how many component cities are contained within or associated with a given entity.
-
B.
hasComponentCity
Indicates that an entity includes or is composed of one or more cities as its constituent parts.
-
C.
hasNumberOfMunicipalities
Indicates the relationship that specifies how many municipalities are associated with or contained within a given administrative or geographic entity.
-
D.
hasUrbanDistrictCount
Indicates the number of urban districts associated with a given entity.
-
E.
hasCountyLevelCity
Indicates that an entity (typically a region or province) includes or administers one or more cities that hold county-level administrative status.
- 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_69d6ad9f464c81909db36d7e96e34b9e |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94e1888b48190bd750f839a26e99e |
completed | April 10, 2026, 7:23 p.m. |
| PD | Predicate disambiguation | batch_69d94d354b488190adc83fb4f2770dd5 |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:55 p.m.