Triple
T101658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philippines |
E2051
|
entity |
| Predicate | numberOfIslands |
P6394
|
FINISHED |
| Object | over 7000 |
—
|
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: over 7000 | Statement: [Philippines, numberOfIslands, over 7000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfIslands Context triple: [Philippines, numberOfIslands, over 7000]
-
A.
nearestLargeIsland
Indicates that one entity is the closest geographically among all islands considered "large" relative to another reference entity.
-
B.
islandSize
Indicates the size or area measurement associated with a particular island.
-
C.
numberOfProvinces
Indicates the total count of provinces associated with a given entity or within a specified region or country.
-
D.
hasIsland
Indicates that one entity possesses, contains, or includes an island as part of its domain, territory, or structure.
-
E.
numberOfRegions
Indicates the total count of distinct regions associated with or contained within a given 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_69a24e0a5b7c81908d52da08c60dabc4 |
completed | Feb. 28, 2026, 2:08 a.m. |
| NER | Named-entity recognition | batch_69a25760af348190bf402089c240887d |
completed | Feb. 28, 2026, 2:48 a.m. |
| PD | Predicate disambiguation | batch_69a2563921f8819087f720b1c803579f |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a2575d8a648190ad8e10d4b04e5e07 |
completed | Feb. 28, 2026, 2:47 a.m. |
Created at: Feb. 28, 2026, 2:12 a.m.