Triple
T34800796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London–Cardiff |
E1003212
|
entity |
| Predicate | hasApproximateRoadDistanceKilometres |
P22795
|
FINISHED |
| Object | 240 |
—
|
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: 240 | Statement: [London–Cardiff, hasApproximateRoadDistanceKilometres, 240]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateRoadDistanceKilometres Context triple: [London–Cardiff, hasApproximateRoadDistanceKilometres, 240]
-
A.
approximateRouteLength
Indicates the estimated total distance or length of a given route, rather than its exact measured value.
-
B.
hasApproximateDrivingDistanceFrom
Indicates that one entity is located at an estimated or approximate driving distance from another entity, typically measured along road routes rather than as a precise or exact value.
-
C.
approximateDistanceKm
chosen
Indicates the estimated distance between two entities measured in kilometers, typically with some degree of inaccuracy or approximation.
-
D.
depthApproxKm
Indicates the approximate depth of something measured in kilometers.
-
E.
trailDistanceApprox
Indicates that the distance along a trail between two locations is approximately a specified value, allowing for some margin of error.
- 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_69f76db543808190b188c6c86a91491b |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69fd91a5dad8819093eeeef527027890 |
completed | May 8, 2026, 7:32 a.m. |
| PD | Predicate disambiguation | batch_69fd8f65fe9081908902500a3228d935 |
completed | May 8, 2026, 7:23 a.m. |
Created at: May 3, 2026, 3:59 p.m.