Triple
T20720853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KSFB |
E509306
|
entity |
| Predicate | distanceFromOrlandoDowntownMiles |
P71076
|
FINISHED |
| Object | approximately 18 |
—
|
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 18 | Statement: [KSFB, distanceFromOrlandoDowntownMiles, approximately 18]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromOrlandoDowntownMiles Context triple: [KSFB, distanceFromOrlandoDowntownMiles, approximately 18]
-
A.
distanceFromOrlando
chosen
Indicates the measured spatial distance between a given place or object and the location of Orlando.
-
B.
distanceFromMiami
Indicates the spatial distance between a given entity’s location and the city of Miami.
-
C.
distanceToDaytonaBeach
Indicates the spatial distance between a given location or entity and Daytona Beach.
-
D.
distanceFromDowntown
Indicates the physical distance between a given location and the central downtown area.
-
E.
distanceToFlorida
Indicates the spatial distance between a given entity’s location and the state of Florida.
- 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_69e0b4c4cc648190b45fda6e2b20af56 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c1d5eec08190832ffba8a90f3076 |
completed | April 21, 2026, 12:16 a.m. |
| PD | Predicate disambiguation | batch_69e5c04b31248190b9b9d91b5cb854e3 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 12:27 p.m.