Triple
T28228085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jose Panganiban |
E711640
|
entity |
| Predicate | distanceToProvinceCapital |
P117649
|
FINISHED |
| Object | approximately 40 kilometers |
—
|
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: approximately 40 kilometers | Statement: [Jose Panganiban, distanceToProvinceCapital, approximately 40 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToProvinceCapital Context triple: [Jose Panganiban, distanceToProvinceCapital, approximately 40 kilometers]
-
A.
distanceToProvinceCapital_km
chosen
Indicates the distance, measured in kilometers, between a given location and the capital city of its province.
-
B.
distanceFromRegionalCapital
Indicates the measured spatial distance between a given place and its corresponding regional capital.
-
C.
distanceToDepartmentCapital
Indicates the measured distance between a given location and the capital city of its corresponding department.
-
D.
distanceFromCapital
Indicates the measured distance between a given location and the capital city of its corresponding region or country.
-
E.
prefecturalCapitalDistanceRelation
Indicates a spatial relationship specifying the distance between an entity and the capital city of its prefecture.
- 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_69efb51dfb048190ada79b745c33b363 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69ffcc6182b48190afb598ced6500e66 |
completed | May 10, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69ffcbb363748190bc6f8d038fba44ff |
completed | May 10, 2026, 12:05 a.m. |
Created at: April 27, 2026, 10:51 p.m.