Triple
T21996511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | フィリピン・ルソン島 |
E543217
|
entity |
| Predicate | 人口集中 |
P70368
|
FINISHED |
| Object | フィリピンで最も人口が多い島 |
—
|
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: フィリピンで最も人口が多い島 | Statement: [フィリピン・ルソン島, 人口集中, フィリピンで最も人口が多い島]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 人口集中 Context triple: [フィリピン・ルソン島, 人口集中, フィリピンで最も人口が多い島]
-
A.
populationConcentration
Indicates the degree to which a population is densely gathered or distributed within a specific area or region.
-
B.
hasPopulationConcentrationIn
chosen
Indicates that a population is densely or significantly clustered within a specified geographic area or region.
-
C.
populationFocus
Indicates that something is primarily directed toward, concerned with, or designed for a particular population or demographic group.
-
D.
populationScale
Indicates the relative size or magnitude of a population, typically categorizing it into broad scale levels (e.g., small, medium, large).
-
E.
largestUrbanConcentrationIn
Indicates that an entity represents the biggest or most populous urban area located within a specified geographic 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_69e11e2c814c8190837d072789000486 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f12765fb0c81908f7b7acda065ee2f |
completed | April 28, 2026, 9:32 p.m. |
| PD | Predicate disambiguation | batch_69e6f62dc9d88190ae387f145f9528de |
completed | April 21, 2026, 3:59 a.m. |
Created at: April 16, 2026, 8:19 p.m.