Triple
T11268870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khyber Pass Railway |
E266757
|
entity |
| Predicate | minimumCurveRadius |
P51538
|
FINISHED |
| Object | 183 m |
—
|
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: 183 m | Statement: [Khyber Pass Railway, minimumCurveRadius, 183 m]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: minimumCurveRadius Context triple: [Khyber Pass Railway, minimumCurveRadius, 183 m]
-
A.
minimumRadiusOfCurves
chosen
Indicates the smallest allowable radius for any curve in a path, track, or route associated with the subject.
-
B.
curveRadius
Indicates the radius of curvature associated with an object or path, describing how sharply it bends at a given point.
-
C.
minimumMass
Indicates the smallest mass value that an entity or system is required, allowed, or observed to have within a given context.
-
D.
minimumRange
Indicates the smallest allowable or observed value within a specified range for a given relationship or measurement.
-
E.
minimumDegree
Indicates that the relationship specifies the smallest number of connections or edges incident to any entity within a given structure or set.
- 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_69d6aac8c2f48190ad0596f1f89f0470 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e94f60d48190bc925c3cb88641a8 |
completed | April 9, 2026, 6 p.m. |
| PD | Predicate disambiguation | batch_69d7879bc56c8190b2e8d2193f29de05 |
completed | April 9, 2026, 11:03 a.m. |
Created at: April 8, 2026, 9:31 p.m.