Triple
T9241599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manila–Tokyo |
E222071
|
entity |
| Predicate | approximateGreatCircleDistance |
P22795
|
FINISHED |
| Object | about 3000 km |
—
|
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: about 3000 km | Statement: [Manila–Tokyo, approximateGreatCircleDistance, about 3000 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateGreatCircleDistance Context triple: [Manila–Tokyo, approximateGreatCircleDistance, about 3000 km]
-
A.
approximateGreatCircleDistanceMiles
Indicates the approximate distance, measured in miles, between two locations along the great-circle path on the surface of a sphere (typically the Earth).
-
B.
distanceBetweenParallels
Indicates the perpendicular distance separating two parallel lines or planes.
-
C.
approximateDistanceKm
chosen
Indicates the estimated distance between two entities measured in kilometers, typically with some degree of inaccuracy or approximation.
-
D.
approximateRouteLength
Indicates the estimated total distance or length of a given route, rather than its exact measured value.
-
E.
flightDistance
Indicates the measured distance covered by a flight between its origin and destination.
- 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_69ca83ee26cc81909ac624e190597d6d |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cd03ea9d90819096f9ca5321dffd56 |
completed | April 1, 2026, 11:39 a.m. |
| PD | Predicate disambiguation | batch_69cc7a4765648190aa9445c4a22dc471 |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:30 p.m.