Triple
T2519726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quezon City |
E55493
|
entity |
| Predicate | hasNumberOfCongressionalDistricts |
P1679
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [Quezon City, hasNumberOfCongressionalDistricts, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfCongressionalDistricts Context triple: [Quezon City, hasNumberOfCongressionalDistricts, 6]
-
A.
hasNumberOfConstituencies
Indicates the specific count of constituencies associated with an entity.
-
B.
numberOfDistricts
chosen
Indicates the total count of districts associated with a given entity or area.
-
C.
hasNumberOfCounties
Indicates the relationship that specifies how many counties are associated with or contained within a given entity.
-
D.
congressionalDistrict
Indicates that one entity is a congressional district that politically represents or geographically contains the other entity.
-
E.
eachDistrictElects
Indicates that every electoral district selects or chooses its own representative or set of representatives.
- 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_69ab49e4749c8190813311efd1630f1b |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd5a33234819082ad49fa6594b6be |
completed | March 7, 2026, 7:37 a.m. |
| PD | Predicate disambiguation | batch_69abd0bf37c0819088d28b5081ba7556 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:46 p.m.