Triple
T32292495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Algrange |
E824999
|
entity |
| Predicate | distanceToLuxembourgBorderKilometers |
P204235
|
FINISHED |
| Object | approximately 10 |
—
|
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 10 | Statement: [Algrange, distanceToLuxembourgBorderKilometers, approximately 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToLuxembourgBorderKilometers Context triple: [Algrange, distanceToLuxembourgBorderKilometers, approximately 10]
-
A.
distanceToLuxembourgCityKilometers
Indicates the physical distance, measured in kilometers, between the given entity and Luxembourg City.
-
B.
distanceToAustrianBorder
Indicates the measured spatial distance between a given entity’s location and the border of Austria.
-
C.
distanceToBulgarianBorder_km
Indicates the distance, measured in kilometers, from a given location to the nearest point on the Bulgarian national border.
-
D.
distanceToBasel_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Basel.
-
E.
distanceFromNiceByRoad_km
Indicates the length of the road route, in kilometers, from the city of Nice to the given location.
- 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_69f349101b788190b4f14884dc7d1ed2 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a0353887e848190ad98ecbbe7bbc061 |
completed | May 12, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_6a0352dfb5648190b9f8c9b7c388d2a1 |
completed | May 12, 2026, 4:18 p.m. |
| PDg | Predicate description generation | batch_6a0353877d4c8190a461118bc66d6b09 |
completed | May 12, 2026, 4:21 p.m. |
Created at: May 1, 2026, 12:44 a.m.