Triple
T6266792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lom, Norway |
E140432
|
entity |
| Predicate | hasCountyCapitalDistance |
P69055
|
FINISHED |
| Object | about 160 km from Lillehammer |
—
|
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 160 km from Lillehammer | Statement: [Lom, Norway, hasCountyCapitalDistance, about 160 km from Lillehammer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCountyCapitalDistance Context triple: [Lom, Norway, hasCountyCapitalDistance, about 160 km from Lillehammer]
-
A.
stateCapitalProximity
Indicates the spatial closeness or distance between a state’s capital city and another specified location.
-
B.
districtHeadquartersDistance
Indicates the distance between a place and its corresponding district headquarters.
-
C.
distanceFromCapital
Indicates the measured distance between a given location and the capital city of its corresponding region or country.
-
D.
prefectureCapitalDistance
Indicates the distance between a prefecture and its designated capital city.
-
E.
nearestCountySeat
Indicates that one location is the closest county seat geographically to another 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_69c008cabc4081909723e2547c9d6cc0 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0639fdad081908492c44d369df8c5 |
completed | March 22, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69c05606fb50819082d1a5a91e5030b6 |
completed | March 22, 2026, 8:50 p.m. |
| PDg | Predicate description generation | batch_69c056c965ac8190b938502fa8c74e1b |
completed | March 22, 2026, 8:53 p.m. |
Created at: March 22, 2026, 4:25 p.m.