Triple
T35427055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melonguane |
E1023948
|
entity |
| Predicate | distanceToPhilippineBorder |
P206982
|
FINISHED |
| Object | approximately 100–150 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 100–150 kilometers | Statement: [Melonguane, distanceToPhilippineBorder, approximately 100–150 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToPhilippineBorder Context triple: [Melonguane, distanceToPhilippineBorder, approximately 100–150 kilometers]
-
A.
nearestPhilippineIsland
Indicates the relationship where a specified location is associated with the Philippine island that is geographically closest to it.
-
B.
distanceFromLuzon
Indicates the measured spatial distance between a given entity or location and the region of Luzon.
-
C.
distanceFromManila
Indicates the measured spatial distance between a given entity’s location and the city of Manila.
-
D.
distanceFromDavaoCity
Indicates the measured spatial distance between a given location and Davao City.
-
E.
nearestPhilippineProvince
Indicates that one entity is the Philippine province geographically closest to the other entity.
- F. None of above. chosen
Provenance (4 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_69f76df6704081909900c60be10d5849 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0324d08190ac5b610cc0f6a38c |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82179081908325a59b8539b3a8 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:03 p.m.