Triple
T4774981
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dallas/Fort Worth International Airport |
E106021
|
entity |
| Predicate | distanceFromDallasDowntownMiles |
P1299
|
FINISHED |
| Object | approximately 20 |
—
|
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 20 | Statement: [Dallas/Fort Worth International Airport, distanceFromDallasDowntownMiles, approximately 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromDallasDowntownMiles Context triple: [Dallas/Fort Worth International Airport, distanceFromDallasDowntownMiles, approximately 20]
-
A.
distanceFromDallas
Indicates the measured distance between a given place or entity and the city of Dallas.
-
B.
distanceFromDowntown
chosen
Indicates the physical distance between a given location and the central downtown area.
-
C.
distanceFromChicagoLoop
Indicates the spatial distance between an entity’s location and the Chicago Loop area.
-
D.
distanceFromFord’sTheatre
Indicates the spatial distance between an entity and Ford’s Theatre.
-
E.
distanceToWashingtonDC
Indicates the physical distance between a given location and Washington, D.C.
- 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_69bd43f3074c8190937e7b0a457fe9f1 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6584481081908f1041a8827e0b42 |
completed | March 20, 2026, 3:19 p.m. |
| PD | Predicate disambiguation | batch_69bd6229d8448190a271719e5e30fd82 |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:21 p.m.