Triple
T25666828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ashland, Alabama |
E643541
|
entity |
| Predicate | distanceToAnnistonInMiles |
P187022
|
FINISHED |
| Object | about 40 |
—
|
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 40 | Statement: [Ashland, Alabama, distanceToAnnistonInMiles, about 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToAnnistonInMiles Context triple: [Ashland, Alabama, distanceToAnnistonInMiles, about 40]
-
A.
distanceToMontgomery
Indicates the spatial distance between a given entity and the location identified as Montgomery.
-
B.
distanceToAnnArborApproxMiles
Indicates an approximate distance, measured in miles, between a given entity’s location and Ann Arbor.
-
C.
distanceToHuntsville
Indicates the measured distance between a given entity and the location Huntsville.
-
D.
distanceToAnnapolisMiles
Indicates the physical distance, measured in miles, between a given location and Annapolis.
-
E.
distanceToAnnapolisInMilesApprox
Indicates the approximate distance, measured in miles, between a given entity and Annapolis.
- 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_69e77e7e45648190a068ed3faa8016ea |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69fb3425666081908916fcbf3b5dd907 |
completed | May 6, 2026, 12:29 p.m. |
| PD | Predicate disambiguation | batch_69fb2f5f3164819099429c2cc3d24e01 |
completed | May 6, 2026, 12:09 p.m. |
| PDg | Predicate description generation | batch_69fb3424724c8190ba55ecf66fa0b171 |
completed | May 6, 2026, 12:29 p.m. |
Created at: April 21, 2026, 7:06 p.m.