Triple
T1195654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toledo |
E25661
|
entity |
| Predicate | distanceToDetroit |
P25679
|
FINISHED |
| Object | approximately 60 miles |
—
|
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 60 miles | Statement: [Toledo, distanceToDetroit, approximately 60 miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToDetroit Context triple: [Toledo, distanceToDetroit, approximately 60 miles]
-
A.
distanceToToronto
Indicates the spatial distance between a given entity’s location and the city of Toronto.
-
B.
distanceToSeattle
Indicates the measured or calculated distance between a given entity’s location and the city of Seattle.
-
C.
distanceToLosAngeles
Indicates the measured or calculated distance between a given entity’s location and the city of Los Angeles.
-
D.
distanceFromToronto
Indicates the spatial distance between a given entity and the location of Toronto.
-
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_69a49429f5ec8190a6a205eb0ae81e5e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd7a756c819085d695acfffeaceb |
completed | March 1, 2026, 10:28 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5d40a08190b7682d8ef8075421 |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bc49693c8190978ec63a5171d342 |
completed | March 1, 2026, 10:23 p.m. |
Created at: March 1, 2026, 7:46 p.m.