Triple
T10475911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Camino de Santiago |
E247043
|
entity |
| Predicate | hasMinimumDistanceForCompostela |
P94234
|
FINISHED |
| Object | 100 kilometers on foot |
—
|
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: 100 kilometers on foot | Statement: [Camino de Santiago, hasMinimumDistanceForCompostela, 100 kilometers on foot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMinimumDistanceForCompostela Context triple: [Camino de Santiago, hasMinimumDistanceForCompostela, 100 kilometers on foot]
-
A.
distanceToPamplona
Indicates the spatial distance between a given entity and the location of Pamplona.
-
B.
distanceToSalamanca
Indicates the spatial distance between a given entity and the location of Salamanca.
-
C.
distanceToMadrid
Indicates the physical distance between a given location or entity and the city of Madrid.
-
D.
distanceFromLisbon
Indicates the measured spatial distance between a given entity’s location and the city of Lisbon.
-
E.
distanceFromPorto
Indicates the measured distance between a given place or entity and the city of Porto.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69d4fe058fcc81909428137d9ffd6d90 |
completed | April 7, 2026, 12:52 p.m. |
Created at: April 6, 2026, 12:21 p.m.