Triple
T2594675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thionville |
E58199
|
entity |
| Predicate | distanceToLuxembourgCityKilometers |
P40111
|
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: [Thionville, distanceToLuxembourgCityKilometers, about 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToLuxembourgCityKilometers Context triple: [Thionville, distanceToLuxembourgCityKilometers, about 30]
-
A.
cityDistanceFromBrussels_km
Indicates the distance, measured in kilometers, between a given city and Brussels.
-
B.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
C.
cityDistanceFromBrussels_miles
Indicates the physical distance, measured in miles, between a given city and Brussels.
-
D.
distanceToKinshasa
Indicates the measured spatial distance between a given entity’s location and the city of Kinshasa.
-
E.
distanceToGeneva
Indicates the spatial distance between a given entity and the location of Geneva.
- 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_69ab4ac14040819098b13f4a27d5c8ff |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd427f58c8190af1c1a9724158c96 |
completed | March 7, 2026, 7:30 a.m. |
| PD | Predicate disambiguation | batch_69abd0d344988190a18dd93b13e002e6 |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd2baee308190bdaa41ef1f6bc9cc |
completed | March 7, 2026, 7:24 a.m. |
Created at: March 6, 2026, 9:49 p.m.