Triple
T2964491
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grosse Pointe Shores, Michigan, United States |
E80127
|
entity |
| Predicate | distanceToDowntownDetroitMiles |
P1299
|
FINISHED |
| Object | approximately 13 |
—
|
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 13 | Statement: [Grosse Pointe Shores, Michigan, United States, distanceToDowntownDetroitMiles, approximately 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToDowntownDetroitMiles Context triple: [Grosse Pointe Shores, Michigan, United States, distanceToDowntownDetroitMiles, approximately 13]
-
A.
distanceToDetroit
Indicates the measured or calculated spatial distance between a given entity and the location of Detroit.
-
B.
distanceFromDowntown
chosen
Indicates the physical distance between a given location and the central downtown area.
-
C.
distanceToMadison
Indicates the spatial distance between a given entity and the location identified as Madison.
-
D.
distanceToMilwaukee
Indicates the measured or calculated spatial distance between a given entity’s location and the city of Milwaukee.
-
E.
distanceFromChicagoLoop
Indicates the spatial distance between an entity’s location and the Chicago Loop area.
- 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_69ad8b1341848190bd19dbf46892887d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9958b1e48190a77f37bf63333c5b |
completed | March 8, 2026, 3:44 p.m. |
| PD | Predicate disambiguation | batch_69ad960e71f8819088179d11248c6ed0 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:58 p.m.