Triple
T8656008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Englewood, Ohio |
E205416
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object |
Union, Ohio
Union, Ohio is a small city in Montgomery County that forms part of the Dayton metropolitan area.
|
E751977
|
NE FINISHED |
How this triple was built (4 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: Union, Ohio | Statement: [Englewood, Ohio, borders, Union, Ohio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Union, Ohio Context triple: [Englewood, Ohio, borders, Union, Ohio]
-
A.
Alliance, Ohio
Alliance, Ohio is a small city in northeastern Ohio known historically for its manufacturing industry and as a regional rail and transportation hub.
-
B.
Hudson, Ohio
Hudson, Ohio is a small city in northeastern Ohio known for its historic New England–style downtown and as a center of education and culture in the region.
-
C.
Collins, Ohio
Collins, Ohio is an unincorporated rural community located within Huron County in the U.S. state of Ohio.
-
D.
Niles, Ohio
Niles, Ohio is a small industrial city in northeastern Ohio best known as the birthplace of U.S. President William McKinley.
-
E.
Lebanon, Ohio
Lebanon, Ohio is a historic small city in Warren County known for its preserved 19th-century downtown, antique shops, and role as a regional cultural and commercial center between Cincinnati and Dayton.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Union, Ohio Triple: [Englewood, Ohio, borders, Union, Ohio]
Generated description
Union, Ohio is a small city in Montgomery County that forms part of the Dayton metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Union, Ohio Target entity description: Union, Ohio is a small city in Montgomery County that forms part of the Dayton metropolitan area.
-
A.
Alliance, Ohio
Alliance, Ohio is a small city in northeastern Ohio known historically for its manufacturing industry and as a regional rail and transportation hub.
-
B.
Hudson, Ohio
Hudson, Ohio is a small city in northeastern Ohio known for its historic New England–style downtown and as a center of education and culture in the region.
-
C.
Collins, Ohio
Collins, Ohio is an unincorporated rural community located within Huron County in the U.S. state of Ohio.
-
D.
Niles, Ohio
Niles, Ohio is a small industrial city in northeastern Ohio best known as the birthplace of U.S. President William McKinley.
-
E.
Lebanon, Ohio
Lebanon, Ohio is a historic small city in Warren County known for its preserved 19th-century downtown, antique shops, and role as a regional cultural and commercial center between Cincinnati and Dayton.
- F. None of above. chosen
Provenance (5 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_69ca8350897c819086cde7596fbe5fe7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc4844586081909b687e278496eefa |
completed | March 31, 2026, 10:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cef373a22c8190931b4107c68e7017 |
completed | April 2, 2026, 10:53 p.m. |
| NEDg | Description generation | batch_69cef52000048190bc5451cfb6446ced |
completed | April 2, 2026, 11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cef809df548190b4f9ecc709b3b065 |
completed | April 2, 2026, 11:13 p.m. |
Created at: March 30, 2026, 6:29 p.m.