Triple
T4367996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gary/Chicago International Airport |
E98824
|
entity |
| Predicate | distanceFromDowntownChicagoMiles |
P24863
|
FINISHED |
| Object | approximately 25 |
—
|
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 25 | Statement: [Gary/Chicago International Airport, distanceFromDowntownChicagoMiles, approximately 25]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromDowntownChicagoMiles Context triple: [Gary/Chicago International Airport, distanceFromDowntownChicagoMiles, approximately 25]
-
A.
distanceFromChicagoLoop
Indicates the spatial distance between an entity’s location and the Chicago Loop area.
-
B.
distanceToChicagoLoop
chosen
Indicates the spatial distance between a given location and Chicago’s central business district (the Loop).
-
C.
distanceFromDowntown
Indicates the physical distance between a given location and the central downtown area.
-
D.
distanceToMilwaukee
Indicates the measured or calculated spatial distance between a given entity’s location and the city of Milwaukee.
-
E.
distanceToMadison
Indicates the spatial distance between a given entity and the location identified as Madison.
- 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_69b3454db3708190aeafd814413c4c3d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352034d3881909ed4b2f9eef5e823 |
completed | March 12, 2026, 11:53 p.m. |
| PD | Predicate disambiguation | batch_69b34f53e3cc8190bf5d4dbe2413bf65 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:17 p.m.