Triple
T26074373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alverca railway station |
E657638
|
entity |
| Predicate | hasApproximateDistanceToLisbon |
P65315
|
FINISHED |
| Object | about 20 km north |
—
|
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 20 km north | Statement: [Alverca railway station, hasApproximateDistanceToLisbon, about 20 km north]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateDistanceToLisbon Context triple: [Alverca railway station, hasApproximateDistanceToLisbon, about 20 km north]
-
A.
distanceFromLisbon
chosen
Indicates the measured spatial distance between a given entity’s location and the city of Lisbon.
-
B.
distanceFromPorto
Indicates the measured distance between a given place or entity and the city of Porto.
-
C.
distanceFromSantarém
Indicates the measured distance between a given entity or location and the city of Santarém.
-
D.
distanceToCascaisApproxKm
Indicates the approximate distance, measured in kilometers, from a given location to Cascais.
-
E.
distanceFromMadeiraIsland
Indicates the spatial distance between an entity and Madeira Island.
- 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_69ee5bbe539081909efc7f9dd7c1b53c |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f66c5c13808190887180099745673b |
completed | May 2, 2026, 9:27 p.m. |
| PD | Predicate disambiguation | batch_69f66abddc448190a488852f8abdeb2c |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 26, 2026, 7:32 p.m.