Triple
T22313678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Santa Cruz Island |
E551586
|
entity |
| Predicate | distanceFromZamboangaCityMainland |
P147708
|
FINISHED |
| Object | approximately 4 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 4 kilometers | Statement: [Great Santa Cruz Island, distanceFromZamboangaCityMainland, approximately 4 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromZamboangaCityMainland Context triple: [Great Santa Cruz Island, distanceFromZamboangaCityMainland, approximately 4 kilometers]
-
A.
distanceToZamboangaCityByRoad_km
Indicates the road travel distance, measured in kilometers, from an entity’s location to Zamboanga City.
-
B.
distanceFromDavaoCity
Indicates the measured spatial distance between a given location and Davao City.
-
C.
distanceToDavaoCity
Indicates the measured distance between a given entity’s location and Davao City.
-
D.
distanceFromManila
Indicates the measured spatial distance between a given entity’s location and the city of Manila.
-
E.
distanceFromPuertoPrincesaKilometers
Indicates the physical distance, measured in kilometers, between a given location and Puerto Princesa.
- 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_69e11e4776588190abb21e5cea79973f |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15750f76c81909d6f788928f503f1 |
completed | April 29, 2026, 12:56 a.m. |
| PD | Predicate disambiguation | batch_69e73004d9e88190bb862319a5aea06b |
completed | April 21, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e7342ce08c8190bc0a7085f4a952e7 |
completed | April 21, 2026, 8:24 a.m. |
Created at: April 16, 2026, 8:42 p.m.