Triple
T4895294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lorain County |
E109660
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Lorain |
E619441
|
NE 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: Lorain | Statement: [Lorain County, hasCity, Lorain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lorain Context triple: [Lorain County, hasCity, Lorain]
-
A.
Lorain
chosen
Lorain is an industrial city on Lake Erie in northern Ohio, historically known for its steel production and shipbuilding.
-
B.
Youngstown
Youngstown is an industrial city in northeastern Ohio historically known for its steel production and central role in the Rust Belt’s economic rise and decline.
-
C.
Akron
Akron is an industrial city in northeastern Ohio known historically for its rubber and tire manufacturing industry.
-
D.
Cleves
Cleves is a historic town in western Germany near the Dutch border, known for its medieval castle and role as a former ducal capital in the Lower Rhine region.
-
E.
Canton, Ohio
Canton, Ohio is a mid-sized city in northeastern Ohio known for its industrial heritage and as the home of the Pro Football Hall of Fame.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69bd4410bbf88190aad50d2451c863d6 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e27cdf48190bb9bd13bd25b887e |
completed | March 20, 2026, 3:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c72f5aca208190824a611c784ab1a1 |
completed | March 28, 2026, 1:31 a.m. |
Created at: March 20, 2026, 1:28 p.m.