Triple
T12967907
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chelsea, Michigan |
E321314
|
entity |
| Predicate | distanceToAnnArborApproxMiles |
P107741
|
FINISHED |
| Object | 15 |
—
|
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: 15 | Statement: [Chelsea, Michigan, distanceToAnnArborApproxMiles, 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToAnnArborApproxMiles Context triple: [Chelsea, Michigan, distanceToAnnArborApproxMiles, 15]
-
A.
distanceToDetroit
Indicates the measured or calculated spatial distance between a given entity and the location of Detroit.
-
B.
distanceToMadison
Indicates the spatial distance between a given entity and the location identified as Madison.
-
C.
distanceToMilwaukee
Indicates the measured or calculated spatial distance between a given entity’s location and the city of Milwaukee.
-
D.
distanceToMarquetteInMiles
Indicates the numerical distance, measured in miles, between a given entity’s location and Marquette.
-
E.
distanceToMonroe
Indicates the measured distance between a given entity and the location named Monroe.
- F. None of above. chosen
Provenance (4 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_69d80763bd6c819094437da5b20b01d2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97dba57988190b786ffed55687a72 |
completed | April 10, 2026, 10:46 p.m. |
| PDg | Predicate description generation | batch_69d97e5811f481908178fac6d2e0efcd |
completed | April 10, 2026, 10:48 p.m. |
Created at: April 9, 2026, 8:31 p.m.