Triple
T34293780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Premià de Mar |
E879964
|
entity |
| Predicate | distanceToBarcelona_km |
P74868
|
FINISHED |
| Object | approximately 20 |
—
|
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 20 | Statement: [Premià de Mar, distanceToBarcelona_km, approximately 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBarcelona_km Context triple: [Premià de Mar, distanceToBarcelona_km, approximately 20]
-
A.
distanceToBarcelonaKm
chosen
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Barcelona.
-
B.
distanceToMadrid
Indicates the physical distance between a given location or entity and the city of Madrid.
-
C.
distanceToBilbao
Indicates the measured distance between a given entity’s location and the city of Bilbao.
-
D.
distanceTo Palma de Mallorca (approximate km)
Indicates the approximate distance in kilometers between an entity’s location and Palma de Mallorca.
-
E.
distanceFromLleida
Indicates the spatial distance measured from the reference location of Lleida to another entity.
- 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_69f349b6df1c81908e5e5b6c2ab6409b |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379fbe4a08190bfe65ebd141164e9 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:57 a.m.