Triple
T28291094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laiya Beach |
E713427
|
entity |
| Predicate | distanceFromManilaByRoad_km |
P57879
|
FINISHED |
| Object | approximately 120 |
—
|
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 120 | Statement: [Laiya Beach, distanceFromManilaByRoad_km, approximately 120]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromManilaByRoad_km Context triple: [Laiya Beach, distanceFromManilaByRoad_km, approximately 120]
-
A.
distanceFromManila
chosen
Indicates the measured spatial distance between a given entity’s location and the city of Manila.
-
B.
distanceFromDavaoCity
Indicates the measured spatial distance between a given location and Davao City.
-
C.
distanceToZamboangaCityByRoad_km
Indicates the road travel distance, measured in kilometers, from an entity’s location to Zamboanga City.
-
D.
distanceToManilaByAir_km
Indicates the distance in kilometers between an entity and Manila when traveling by air.
-
E.
distanceFromLuzon
Indicates the measured spatial distance between a given entity or location and the region of Luzon.
- 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_69efb52371d88190a1381c4e58a3b731 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69ffe23081408190a121d901dbce1403 |
completed | May 10, 2026, 1:41 a.m. |
| PD | Predicate disambiguation | batch_69ffe18aed348190912a5996b2da728b |
completed | May 10, 2026, 1:38 a.m. |
Created at: April 27, 2026, 11:29 p.m.