Triple
T21650124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gertrude Seiberling |
E534314
|
entity |
| Predicate | placeOfActivity |
P1527
|
FINISHED |
| Object | Akron, Ohio |
—
|
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: Akron, Ohio | Statement: [Gertrude Seiberling, placeOfActivity, Akron, Ohio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Akron, Ohio Context triple: [Gertrude Seiberling, placeOfActivity, Akron, Ohio]
-
A.
Akron
Akron is an industrial city in northeastern Ohio known historically for its rubber and tire manufacturing industry.
-
B.
Akron, Michigan
Akron, Michigan is a small rural village located in Tuscola County in the Thumb region of the U.S. state of Michigan.
-
C.
Hamilton, Ohio
Hamilton, Ohio is a historic industrial city in southwestern Ohio that serves as the county seat of Butler County and is part of the greater Cincinnati–Miami Valley region.
-
D.
Akron, Ohio, United States
chosen
Akron is a mid-sized industrial city in northeastern Ohio, United States, historically known for its rubber and tire manufacturing industry.
-
E.
Cleveland
Cleveland is a small city in northeastern Georgia known as a gateway to the Appalachian Mountains and nearby gold-mining and outdoor recreation areas.
- 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_69e0c466aec88190ba39c7543dbc8ba2 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef5913cd9c81908a6ce9bc741416bf |
completed | April 27, 2026, 12:39 p.m. |
Created at: April 16, 2026, 6:35 p.m.