Triple
T33341195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kango |
E853671
|
entity |
| Predicate | distanceToByRoad |
P121160
|
FINISHED |
| Object | approximately 90 km from Libreville |
—
|
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 90 km from Libreville | Statement: [Kango, distanceToByRoad, approximately 90 km from Libreville]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToByRoad Context triple: [Kango, distanceToByRoad, approximately 90 km from Libreville]
-
A.
distanceFromRoads
Indicates the measured or estimated spatial distance between a location and the nearest road.
-
B.
distanceToNomeByRoad
Indicates the road travel distance between a given place and the city of Nome.
-
C.
distanceToSplitByRoad_km
Indicates the distance in kilometers from a given location to the nearest point where a road splits or branches.
-
D.
roadDistanceRelation
chosen
Indicates a relationship specifying the distance between two locations as measured along a road or road network.
-
E.
approximateRouteLength
Indicates the estimated total distance or length of a given route, rather than its exact measured value.
- 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_69f3496a1a588190bad9cbe9221144e0 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f505c88190ac0879ab422c3054 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:34 a.m.