Triple
T2038780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Radcliff, Kentucky |
E44693
|
entity |
| Predicate | distanceToLouisvilleApprox |
P35478
|
FINISHED |
| Object | about 35 miles |
—
|
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 35 miles | Statement: [Radcliff, Kentucky, distanceToLouisvilleApprox, about 35 miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToLouisvilleApprox Context triple: [Radcliff, Kentucky, distanceToLouisvilleApprox, about 35 miles]
-
A.
distanceToMontgomery
Indicates the spatial distance between a given entity and the location identified as Montgomery.
-
B.
distanceToPhiladelphia
Indicates the spatial distance between a given entity’s location and the city of Philadelphia.
-
C.
distanceToLondon
Indicates the measured distance between a given entity’s location and the city of London.
-
D.
distanceToCharlotte
Indicates the measured or estimated distance between a given entity and the location Charlotte.
-
E.
distanceToKingstonApprox
Indicates an approximate distance between a given location and Kingston.
- 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_69a889159ec481908f9e4472d9f480c7 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb951870481909dbdd8fc8b0c02fe |
completed | March 7, 2026, 5:36 a.m. |
| PD | Predicate disambiguation | batch_69abb7a8125881909c0cb58b777c1faa |
completed | March 7, 2026, 5:29 a.m. |
| PDg | Predicate description generation | batch_69abb90ec7948190bbfb0329e9e67cca |
completed | March 7, 2026, 5:35 a.m. |
Created at: March 4, 2026, 7:39 p.m.