Triple
T26495357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilkesboro, North Carolina |
E669267
|
entity |
| Predicate | distanceToWinstonSalemInMiles |
P42637
|
FINISHED |
| Object | about 55 |
—
|
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 55 | Statement: [Wilkesboro, North Carolina, distanceToWinstonSalemInMiles, about 55]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToWinstonSalemInMiles Context triple: [Wilkesboro, North Carolina, distanceToWinstonSalemInMiles, about 55]
-
A.
distanceToWinstonSalem
chosen
Indicates the spatial distance between a given entity’s location and the city of Winston-Salem.
-
B.
distanceToRaleighApproxMiles
Indicates the approximate distance, measured in miles, between a given place and Raleigh.
-
C.
distanceToGreensboroMiles
Indicates the physical distance, measured in miles, between a given location and Greensboro.
-
D.
distanceToCharlotte
Indicates the measured or estimated distance between a given entity and the location Charlotte.
-
E.
distanceToWilmington
Indicates the measured distance between a given entity’s location and the location of Wilmington.
- F. None of above.
Provenance (3 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_69eeb319007081909642b414b114b35a |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69fe6739d4dc8190ae7505c089bbac29 |
completed | May 8, 2026, 10:44 p.m. |
| PD | Predicate disambiguation | batch_69fe6541dffc81909c66a61ba69f38fc |
completed | May 8, 2026, 10:35 p.m. |
Created at: April 27, 2026, 1:07 a.m.