Triple
T2235386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St Neots |
E49267
|
entity |
| Predicate | distanceToLondon_km |
P8256
|
FINISHED |
| Object | approximately 80 |
—
|
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 80 | Statement: [St Neots, distanceToLondon_km, approximately 80]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToLondon_km Context triple: [St Neots, distanceToLondon_km, approximately 80]
-
A.
distanceToLondon
chosen
Indicates the measured distance between a given entity’s location and the city of London.
-
B.
distanceFromCentralLondon
Indicates the spatial separation or length of travel between a given location and central London.
-
C.
distanceToBerlin
Indicates the spatial distance between a given entity’s location and the city of Berlin.
-
D.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
E.
approximateDistanceKm
Indicates the estimated distance between two entities measured in kilometers, typically with some degree of inaccuracy or approximation.
- 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_69a88aa84bdc819086df50e9c20b301e |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc093ba0c819091df09a0e018fce1 |
completed | March 7, 2026, 6:07 a.m. |
| PD | Predicate disambiguation | batch_69abbdafc07881909101266a33ae7031 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:47 p.m.