Triple
T9612246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Strongsville |
E232128
|
entity |
| Predicate | distanceToCleveland |
P89238
|
FINISHED |
| Object | approximately 15 miles southwest |
—
|
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 15 miles southwest | Statement: [Strongsville, distanceToCleveland, approximately 15 miles southwest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToCleveland Context triple: [Strongsville, distanceToCleveland, approximately 15 miles southwest]
-
A.
distanceToAkronInMilesApproximate
Indicates the approximate distance, measured in miles, between a given entity’s location and Akron.
-
B.
distanceToPittsburgh
Indicates the spatial distance between a given entity’s location and the city of Pittsburgh.
-
C.
distanceToDetroit
Indicates the measured or calculated spatial distance between a given entity and the location of Detroit.
-
D.
distanceToDayton
Indicates the spatial distance between a given entity or location and the city of Dayton.
-
E.
distanceToPhiladelphia
Indicates the spatial distance between a given entity’s location and the city of Philadelphia.
- 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_69ca8485a90c819094fe40b42fde9d70 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a87764481909ab96cd2ab96d14b |
completed | April 1, 2026, 10:21 p.m. |
| PD | Predicate disambiguation | batch_69ccd5a6fd2481908efd131e207b8143 |
completed | April 1, 2026, 8:21 a.m. |
| PDg | Predicate description generation | batch_69ccd93fc45c8190a823305e461e581d |
completed | April 1, 2026, 8:37 a.m. |
Created at: March 30, 2026, 8:09 p.m.