Triple
T24741163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Opelousas, Louisiana |
E618560
|
entity |
| Predicate | distanceToLafayetteInMiles |
P159379
|
FINISHED |
| Object | approximately 21 |
—
|
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 21 | Statement: [Opelousas, Louisiana, distanceToLafayetteInMiles, approximately 21]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToLafayetteInMiles Context triple: [Opelousas, Louisiana, distanceToLafayetteInMiles, approximately 21]
-
A.
distanceToLouisianaBorderInMiles
Indicates the numerical distance, measured in miles, from a given location to the border of the state of Louisiana.
-
B.
distanceToLouisvilleApprox
Indicates an approximate distance between a given location and Louisville.
-
C.
distanceToJeffersonCity
Indicates the spatial distance between a given entity and Jefferson City.
-
D.
distanceToTallahasseeInMiles
Indicates the physical distance, measured in miles, between a given entity’s location and Tallahassee.
-
E.
distanceToMonroe
Indicates the measured distance between a given entity and the location named Monroe.
- 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_69e2fab8f95c81908bb9e552cf3280c2 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f584f07b648190aee894c1d5320bc3 |
completed | May 2, 2026, 5 a.m. |
| PD | Predicate disambiguation | batch_69f4a0edd10c81908a052ab864d57c54 |
completed | May 1, 2026, 12:47 p.m. |
| PDg | Predicate description generation | batch_69f55e497fa081909bc59a7b92c5df59 |
completed | May 2, 2026, 2:15 a.m. |
Created at: April 18, 2026, 4:12 a.m.