Triple
T14761578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marion County, Ohio |
E346872
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object |
Waldo, Ohio
Waldo, Ohio is a small village in central Ohio known for its rural character and location within Marion County.
|
E1118871
|
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: Waldo, Ohio | Statement: [Marion County, Ohio, containsSettlement, Waldo, Ohio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Waldo, Ohio Context triple: [Marion County, Ohio, containsSettlement, Waldo, Ohio]
-
A.
Waverly, Ohio
Waverly, Ohio is a small city in southern Ohio known as the hometown of the country rock band Pure Prairie League.
-
B.
Wilmington, Ohio
Wilmington, Ohio is a small city in southwestern Ohio known historically as a regional transportation hub and home to a major air park and agricultural community.
-
C.
Willard, Ohio
Willard, Ohio is a small city in north-central Ohio known historically as a railroad town and for its agricultural and manufacturing industries.
-
D.
Wakeman, Ohio
Wakeman, Ohio is a small village in northern Ohio known for its rural character and location within Huron County.
-
E.
Wapakoneta, Ohio
Wapakoneta, Ohio is a small city in western Ohio best known as the hometown of astronaut Neil Armstrong and for its strong ties to aerospace history.
- 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: Waldo, Ohio Triple: [Marion County, Ohio, containsSettlement, Waldo, Ohio]
Generated description
Waldo, Ohio is a small village in central Ohio known for its rural character and location within Marion County.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Waldo, Ohio Target entity description: Waldo, Ohio is a small village in central Ohio known for its rural character and location within Marion County.
-
A.
Waverly, Ohio
Waverly, Ohio is a small city in southern Ohio known as the hometown of the country rock band Pure Prairie League.
-
B.
Wilmington, Ohio
Wilmington, Ohio is a small city in southwestern Ohio known historically as a regional transportation hub and home to a major air park and agricultural community.
-
C.
Willard, Ohio
Willard, Ohio is a small city in north-central Ohio known historically as a railroad town and for its agricultural and manufacturing industries.
-
D.
Wakeman, Ohio
Wakeman, Ohio is a small village in northern Ohio known for its rural character and location within Huron County.
-
E.
Wapakoneta, Ohio
Wapakoneta, Ohio is a small city in western Ohio best known as the hometown of astronaut Neil Armstrong and for its strong ties to aerospace history.
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7f207dc819088a53f717736a121 |
completed | April 14, 2026, 11:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe0cf24c0081909221cb7d761e882f |
completed | May 8, 2026, 4:18 p.m. |
| NEDg | Description generation | batch_69fe1913f01c8190917992cbcb8f0b62 |
completed | May 8, 2026, 5:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe19c36dcc8190a8b3565d6e9cec04 |
completed | May 8, 2026, 5:13 p.m. |
Created at: April 10, 2026, 1:30 a.m.