Triple
T12588777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank Seiberling |
E300541
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Akron, Ohio |
E27222
|
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: Akron, Ohio | Statement: [Frank Seiberling, residence, Akron, Ohio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Akron, Ohio Context triple: [Frank Seiberling, residence, Akron, Ohio]
-
A.
Akron
chosen
Akron is an industrial city in northeastern Ohio known historically for its rubber and tire manufacturing industry.
-
B.
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.
-
C.
Cleveland
Cleveland is a major city in the U.S. state of Ohio, known for its industrial history, cultural institutions like the Rock and Roll Hall of Fame, and its location on the southern shore of Lake Erie.
-
D.
Cleveland
Cleveland is a common English surname most prominently associated with Grover Cleveland, the 22nd and 24th president of the United States.
-
E.
Cleveland
Cleveland is a fictional character who serves as the central protagonist in the story "The Pirate."
- 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_69d7bde87b648190bcd0266e9efde098 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954bd5e8c8190a2f233b91682341f |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6fef515488190957a69e1cc901d65 |
completed | May 3, 2026, 7:53 a.m. |
Created at: April 9, 2026, 5:06 p.m.