Triple
T22025393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlies Bunion |
E543947
|
entity |
| Predicate | distanceFromNewfoundGap |
P146308
|
FINISHED |
| Object | about 4 miles one way |
—
|
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 4 miles one way | Statement: [Charlies Bunion, distanceFromNewfoundGap, about 4 miles one way]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromNewfoundGap Context triple: [Charlies Bunion, distanceFromNewfoundGap, about 4 miles one way]
-
A.
distanceToGreatSmokyMountainsNationalPark
Indicates the spatial distance between an entity and Great Smoky Mountains National Park.
-
B.
distanceFromBoston
Indicates the spatial distance between a given entity’s location and the city of Boston.
-
C.
distanceFromStJohns
Indicates the measured distance between a given location and St. Johns.
-
D.
distanceToNewport
Indicates the measured or calculated distance between a given entity and the location named Newport.
-
E.
distanceFromFairbanks
Indicates the spatial distance between a given location or entity and the city of Fairbanks.
- 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_69e11e2e8ea4819084210fe06d3a1b8d |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127cba61c8190a48ec2c1ee1315b0 |
completed | April 28, 2026, 9:34 p.m. |
| PD | Predicate disambiguation | batch_69e6f63b0d048190b241622759aab9de |
completed | April 21, 2026, 3:59 a.m. |
| PDg | Predicate description generation | batch_69e6fad4a540819096cdd5ea08527220 |
completed | April 21, 2026, 4:19 a.m. |
Created at: April 16, 2026, 8:24 p.m.