Triple
T23096212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arcos de la Frontera |
E575895
|
entity |
| Predicate | distanceToJerezDeLaFrontera |
P150904
|
FINISHED |
| Object | about 30 kilometres |
—
|
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 30 kilometres | Statement: [Arcos de la Frontera, distanceToJerezDeLaFrontera, about 30 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToJerezDeLaFrontera Context triple: [Arcos de la Frontera, distanceToJerezDeLaFrontera, about 30 kilometres]
-
A.
distanceFromSeville
Indicates the spatial distance separating an entity from the location of Seville.
-
B.
distanceFromCórdobaCity
Indicates the spatial distance between an entity and the city of Córdoba.
-
C.
distanceToGranada
Indicates the spatial distance between a given entity or location and the city of Granada.
-
D.
distanceToMálaga
Indicates the spatial distance between a given entity and the location of Málaga.
-
E.
distanceToMadrid
Indicates the physical distance between a given location or entity and the city of Madrid.
- 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_69e245c060b48190a9bd61a47a16db17 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18de522e48190a37e6c2fda2de465 |
completed | April 29, 2026, 4:49 a.m. |
| PD | Predicate disambiguation | batch_69ef89e5ce748190b2c3ac3843484127 |
completed | April 27, 2026, 4:08 p.m. |
| PDg | Predicate description generation | batch_69ef9b7494f4819088ae59ea3d0ae8ab |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 17, 2026, 3:57 p.m.