Triple
T13181237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vigevano |
E313729
|
entity |
| Predicate | distanceFromPavia |
P108927
|
FINISHED |
| Object | about 30 kilometres 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: about 30 kilometres northwest | Statement: [Vigevano, distanceFromPavia, about 30 kilometres northwest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromPavia Context triple: [Vigevano, distanceFromPavia, about 30 kilometres northwest]
-
A.
distanceFromMilan
Indicates the spatial distance between a given entity and the city of Milan.
-
B.
distanceToPadua
Indicates the measured distance between a given entity’s location and the city of Padua.
-
C.
distanceToBari
Indicates the measured distance between a given entity (such as a place or object) and the location of Bari.
-
D.
distanceToSavona_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Savona.
-
E.
distanceFromRome
Indicates the measured spatial distance between a given entity’s location and the city of Rome.
- 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_69d806ae1e08819090d95bfe1538cc17 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98cf054f88190b05ced98d5a22a62 |
completed | April 10, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69d98bc2c0c88190be357811aa8e828d |
completed | April 10, 2026, 11:46 p.m. |
| PDg | Predicate description generation | batch_69d98ceeb22c8190a6be666031d9e5a4 |
completed | April 10, 2026, 11:51 p.m. |
Created at: April 9, 2026, 9:14 p.m.