Triple
T14154342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Denver, North Carolina |
E350771
|
entity |
| Predicate | distanceToCharlotteInMiles |
P28399
|
FINISHED |
| Object | approximately 25 |
—
|
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 25 | Statement: [Denver, North Carolina, distanceToCharlotteInMiles, approximately 25]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToCharlotteInMiles Context triple: [Denver, North Carolina, distanceToCharlotteInMiles, approximately 25]
-
A.
distanceToCharlotte
chosen
Indicates the measured or estimated distance between a given entity and the location Charlotte.
-
B.
distanceToRaleighApproxMiles
Indicates the approximate distance, measured in miles, between a given place and Raleigh.
-
C.
distanceToCharlestonMi
Indicates the measured distance, in miles, from a given entity or location to Charleston.
-
D.
distanceToWinstonSalem
Indicates the spatial distance between a given entity’s location and the city of Winston-Salem.
-
E.
distanceToGreensboroMiles
Indicates the physical distance, measured in miles, between a given location and Greensboro.
- 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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6133754881908e1e97db71772deb |
completed | April 14, 2026, 3:45 p.m. |
| PD | Predicate disambiguation | batch_69de05b8434c81908c33b1b513463b12 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 12:58 a.m.