Triple
T24585781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Pietro in Casale |
E608382
|
entity |
| Predicate | distanceToBologna_km |
P156452
|
FINISHED |
| Object | about 30 |
—
|
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 | Statement: [San Pietro in Casale, distanceToBologna_km, about 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBologna_km Context triple: [San Pietro in Casale, distanceToBologna_km, about 30]
-
A.
distanceToBergamo
Indicates the spatial distance between a given entity and the location of Bergamo.
-
B.
distanceFromMilan
Indicates the spatial distance between a given entity and the city of Milan.
-
C.
distanceToGenoa_km
Indicates the physical distance, measured in kilometers, between a given place or object and the city of Genoa.
-
D.
distanceToVenice_km
Indicates the physical distance, measured in kilometers, between a given place and the city of Venice.
-
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_69e2c4ce89248190ad99e18f0638dfbb |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a986dd6481909a2445b697962e09 |
completed | April 30, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69f2a6c1f07081908edf0b521767e79b |
completed | April 30, 2026, 12:48 a.m. |
| PDg | Predicate description generation | batch_69f2a846c5bc81909ba50cee483bea91 |
completed | April 30, 2026, 12:54 a.m. |
Created at: April 18, 2026, 2:29 a.m.