Triple
T11072214
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manila city government |
E261773
|
entity |
| Predicate | numberOfBarangays |
P13208
|
FINISHED |
| Object | 897 |
—
|
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: 897 | Statement: [Manila city government, numberOfBarangays, 897]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfBarangays Context triple: [Manila city government, numberOfBarangays, 897]
-
A.
hasNumberOfBarangays
chosen
Indicates the total count of barangays associated with a given administrative unit or locality.
-
B.
hasComponentBarangays
Indicates that an entity (typically a municipality, city, or similar administrative unit) is composed of or includes specific barangays as its subunits.
-
C.
barangay
Indicates that an entity is associated with, located in, or falls under the jurisdiction of a specific barangay (the smallest local administrative division).
-
D.
rankByAreaInPhilippines
Indicates the relative ordering of entities based on their area size specifically within the Philippines.
-
E.
numberOfLocalGovernmentAreas
Indicates the count of local government areas associated with a given entity or region.
- 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_69d6aa9983c08190b0ef61603b69feac |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7994bbb30819090410bd3d0fde33c |
completed | April 9, 2026, 12:19 p.m. |
| PD | Predicate disambiguation | batch_69d74415403c81909778bcd829e8832e |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:26 p.m.