Triple
T15764254
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sopchoppy, Florida |
E382177
|
entity |
| Predicate | distanceToTallahasseeInMiles |
P120234
|
FINISHED |
| Object | approximately 35 |
—
|
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 35 | Statement: [Sopchoppy, Florida, distanceToTallahasseeInMiles, approximately 35]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToTallahasseeInMiles Context triple: [Sopchoppy, Florida, distanceToTallahasseeInMiles, approximately 35]
-
A.
distanceToFlorida
Indicates the spatial distance between a given entity’s location and the state of Florida.
-
B.
distanceToMontgomery
Indicates the spatial distance between a given entity and the location identified as Montgomery.
-
C.
distanceFromMiami
Indicates the spatial distance between a given entity’s location and the city of Miami.
-
D.
distanceToAtlanta
Indicates the measured or calculated distance between a given location and the city of Atlanta.
-
E.
distanceFromDallas
Indicates the measured distance between a given place or entity and the city of Dallas.
- 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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e050b6c9fc8190a1bcf763c4b04b12 |
completed | April 16, 2026, 3 a.m. |
| PD | Predicate disambiguation | batch_69e00531e7ac8190a4190cce4f7fab4c |
completed | April 15, 2026, 9:37 p.m. |
| PDg | Predicate description generation | batch_69e03cc871d0819085c0fc54de7984ff |
completed | April 16, 2026, 1:35 a.m. |
Created at: April 10, 2026, 4:47 a.m.