Triple
T19601162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenton station |
E470478
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Kenton |
—
|
NE NERFINISHED |
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: Kenton | Statement: [Kenton station, locatedIn, Kenton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kenton Context triple: [Kenton station, locatedIn, Kenton]
-
A.
Kenton
chosen
Kenton is a suburban district in northwest London known for its residential character and local shopping streets.
-
B.
Kenton
Kenton is a masculine given name of English origin, used both as a first name and surname.
-
C.
Kenton, Ohio
Kenton, Ohio is a small city in northwestern Ohio that serves as the county seat of Hardin County and a local center for agriculture and manufacturing.
-
D.
Garfield Heights
Garfield Heights is a suburban city located southeast of downtown Cleveland in northeastern Ohio.
-
E.
Kenton, United States
Kenton is a small town in the United States known, among other things, for its international twinning relationship with Neuilly-sur-Seine in France.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e510024481908415c0d616fa6186 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e6407f1fd88190aa82c4c96f755584 |
completed | April 20, 2026, 3:04 p.m. |
Created at: April 10, 2026, 1:43 p.m.