Triple
T15054418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alpignano |
E379449
|
entity |
| Predicate | geographicDirectionFromTurin |
P117145
|
FINISHED |
| Object | northwest |
—
|
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: northwest | Statement: [Alpignano, geographicDirectionFromTurin, northwest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: geographicDirectionFromTurin Context triple: [Alpignano, geographicDirectionFromTurin, northwest]
-
A.
distanceFromTurin
Indicates the spatial distance separating an entity from the location of Turin.
-
B.
directionFromRome
Indicates the cardinal or relative compass direction in which one entity is located when measured from Rome.
-
C.
distanceToTurin
Indicates the spatial distance between a given entity or location and the city of Turin.
-
D.
directionFromParis
Indicates the cardinal or relative compass direction of an entity as measured from Paris toward that entity.
-
E.
distanceToSavona_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Savona.
- 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_69d85cd64d108190853797a95c11cc45 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69deda92091c81909180f486edf01405 |
completed | April 15, 2026, 12:23 a.m. |
| PD | Predicate disambiguation | batch_69deb95a182081908fffc4402b02a394 |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec71e8dcc81908badc834b6ccf273 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:01 a.m.