Triple
T28456744
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paredes de Coura |
E716732
|
entity |
| Predicate | distanceToViana do Castelo |
P201649
|
FINISHED |
| Object | approximately 40 km |
—
|
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 40 km | Statement: [Paredes de Coura, distanceToViana do Castelo, approximately 40 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToViana do Castelo Context triple: [Paredes de Coura, distanceToViana do Castelo, approximately 40 km]
-
A.
distanceFromPorto
Indicates the measured distance between a given place or entity and the city of Porto.
-
B.
distanceFromLisbon
Indicates the measured spatial distance between a given entity’s location and the city of Lisbon.
-
C.
distanceToCascaisApproxKm
Indicates the approximate distance, measured in kilometers, from a given location to Cascais.
-
D.
distanceFromSantarém
Indicates the measured distance between a given entity or location and the city of Santarém.
-
E.
distanceToCoimbra
Indicates the spatial distance between a given entity and the location of Coimbra.
- 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_69efd6b76f8c8190a7ba908aca280942 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_6a0010e46d948190a51111b5270fade7 |
completed | May 10, 2026, 5 a.m. |
| PD | Predicate disambiguation | batch_6a001061d34c8190bfe73f3d7c061eb7 |
completed | May 10, 2026, 4:58 a.m. |
| PDg | Predicate description generation | batch_6a0010e304a08190a4d0a4fa11a9a3b3 |
completed | May 10, 2026, 5 a.m. |
Created at: April 28, 2026, 1:54 a.m.