Triple
T38176883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ann Arbor Municipal Airport |
E1000239
|
entity |
| Predicate | distance from downtown Ann Arbor (miles) |
P107741
|
FINISHED |
| Object | about 4 |
—
|
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: about 4 | Statement: [Ann Arbor Municipal Airport, distance from downtown Ann Arbor (miles), about 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distance from downtown Ann Arbor (miles) Context triple: [Ann Arbor Municipal Airport, distance from downtown Ann Arbor (miles), about 4]
-
A.
distanceToAnnArborApproxMiles
chosen
Indicates an approximate distance, measured in miles, between a given entity’s location and Ann Arbor.
-
B.
distanceToGrandRapidsDowntown
Indicates the physical distance between a given location and downtown Grand Rapids.
-
C.
distanceFromDowntown
Indicates the physical distance between a given location and the central downtown area.
-
D.
distanceToDetroit
Indicates the measured or calculated spatial distance between a given entity and the location of Detroit.
-
E.
distanceFromWarren_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the location of Warren.
- 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_69f76daaace48190a38cee37f8ce343f |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_6a037df1223c8190a5d61e4f8e6fd613 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a037a1ad6c48190bfe35d350c1b4751 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:29 p.m.