Triple
T2327489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenora |
E48321
|
entity |
| Predicate | distanceToWinnipeg |
P38931
|
FINISHED |
| Object | approximately 200 kilometres east |
—
|
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 200 kilometres east | Statement: [Kenora, distanceToWinnipeg, approximately 200 kilometres east]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToWinnipeg Context triple: [Kenora, distanceToWinnipeg, approximately 200 kilometres east]
-
A.
distanceToSaintPaul
Indicates the measured spatial distance between a given entity and the location of Saint Paul.
-
B.
distanceFromToronto
Indicates the spatial distance between a given entity and the location of Toronto.
-
C.
distanceToMinneapolis
Indicates the measured distance between a given entity’s location and the city of Minneapolis.
-
D.
distanceToOttawa
Indicates the spatial distance between a given entity’s location and the city of Ottawa.
-
E.
distanceToToronto
Indicates the spatial distance between a given entity’s location and the city of Toronto.
- 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_69a88aa308a88190b0b86c011fda7fce |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abcc30c5e881908c5d526d7e7491d0 |
completed | March 7, 2026, 6:56 a.m. |
| PD | Predicate disambiguation | batch_69abc5926d048190a535e3f23d41de2a |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abcc2fa25c8190858c1c541b914f4c |
completed | March 7, 2026, 6:56 a.m. |
Created at: March 4, 2026, 7:50 p.m.