Triple
T4729662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angeles City |
E104973
|
entity |
| Predicate | hasBarangayCount |
P13208
|
FINISHED |
| Object | 33 |
—
|
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: 33 | Statement: [Angeles City, hasBarangayCount, 33]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBarangayCount Context triple: [Angeles City, hasBarangayCount, 33]
-
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.
hasNumberOfCouncillors
Indicates the relationship that specifies how many councillors are associated with a given entity.
-
E.
hasNumberOfAdministrativeDong
Indicates the relationship specifying how many administrative dong (smallest local administrative units) are associated with a given entity.
- 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_69bd43ed84648190ae0b7ee8e8d00482 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd67c9c3c08190a6c4944cdd1362a8 |
completed | March 20, 2026, 3:29 p.m. |
| PD | Predicate disambiguation | batch_69bd6220071881909670c89d072ffb6d |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:19 p.m.