Triple
T6073012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amboise |
E135329
|
entity |
| Predicate | distanceFromToursKilometers |
P22795
|
FINISHED |
| Object | about 25 |
—
|
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 25 | Statement: [Amboise, distanceFromToursKilometers, about 25]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromToursKilometers Context triple: [Amboise, distanceFromToursKilometers, about 25]
-
A.
approximateDistanceKm
chosen
Indicates the estimated distance between two entities measured in kilometers, typically with some degree of inaccuracy or approximation.
-
B.
lengthInKm
Indicates that one entity specifies the length or distance of another entity measured in kilometers.
-
C.
range_km
Indicates the maximum distance, measured in kilometers, over which something can operate, travel, or be effective.
-
D.
trailDistanceApprox
Indicates that the distance along a trail between two locations is approximately a specified value, allowing for some margin of error.
-
E.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
- 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_69c00879e8048190b690717d19c5bc03 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05759d29481908912015e734ab943 |
completed | March 22, 2026, 8:55 p.m. |
| PD | Predicate disambiguation | batch_69c049f21fe08190995df3c5c05fb8ea |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:11 p.m.