Triple
T10475943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Camino de Santiago |
E247043
|
entity |
| Predicate | hasApproximateLengthOfCaminoFrances |
P69216
|
FINISHED |
| Object | about 780 kilometers |
—
|
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 780 kilometers | Statement: [Camino de Santiago, hasApproximateLengthOfCaminoFrances, about 780 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateLengthOfCaminoFrances Context triple: [Camino de Santiago, hasApproximateLengthOfCaminoFrances, about 780 kilometers]
-
A.
distanceToPamplona
Indicates the spatial distance between a given entity and the location of Pamplona.
-
B.
trackLengthApproxKm
chosen
Indicates that one entity has an approximate track length, measured in kilometers, associated with it.
-
C.
lengthInPortugal
Indicates that something has a specified length measured within the context or territory of Portugal.
-
D.
navigableLengthApproxKm
Indicates the approximate distance, measured in kilometers, over which something (typically a waterway) can be navigated.
-
E.
lengthInFrance
Indicates that the specified length or duration applies specifically within the context of France (e.g., under French conditions, jurisdiction, or territory).
- 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_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5094f6b408190a5a26b1a82e4a02b |
completed | April 7, 2026, 1:40 p.m. |
| PD | Predicate disambiguation | batch_69d4fb84bafc8190819757b93620508a |
completed | April 7, 2026, 12:41 p.m. |
Created at: April 6, 2026, 12:21 p.m.