Triple
T2271109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clarence, Iowa |
E50658
|
entity |
| Predicate | county |
P75
|
FINISHED |
| Object |
Cedar County
Cedar County is a county in eastern Iowa known for its agricultural landscape and small rural communities, including the town of Clarence.
|
E319567
|
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: Cedar County | Statement: [Clarence, Iowa, county, Cedar County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cedar County Context triple: [Clarence, Iowa, county, Cedar County]
-
A.
Crawford County
Crawford County is a rural county in central Georgia known for its agricultural landscape and small-town communities west of Macon.
-
B.
Barton County
Barton County is a rural county in southwestern Missouri, United States, known as the birthplace of President Harry S. Truman.
-
C.
Cass County
Cass County is a rural county in southwestern Iowa known for its agricultural landscape and small communities.
-
D.
Pike County
Pike County is a county in west-central Georgia, United States, known for its rural character and location within the Atlanta metropolitan area’s broader region.
-
E.
Pike County
Pike County is a county in southeastern Alabama known for its agricultural economy and as the home of Troy University.
- 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: Cedar County Triple: [Clarence, Iowa, county, Cedar County]
Generated description
Cedar County is a county in eastern Iowa known for its agricultural landscape and small rural communities, including the town of Clarence.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cedar County Target entity description: Cedar County is a county in eastern Iowa known for its agricultural landscape and small rural communities, including the town of Clarence.
-
A.
Crawford County
Crawford County is a rural county in central Georgia known for its agricultural landscape and small-town communities west of Macon.
-
B.
Barton County
Barton County is a rural county in southwestern Missouri, United States, known as the birthplace of President Harry S. Truman.
-
C.
Cass County
Cass County is a rural county in southwestern Iowa known for its agricultural landscape and small communities.
-
D.
Pike County
Pike County is a county in southeastern Alabama known for its agricultural economy and as the home of Troy University.
-
E.
Pike County
Pike County is a county in west-central Georgia, United States, known for its rural character and location within the Atlanta metropolitan area’s broader region.
- 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_69a88b05910c8190a9a2b1ff230c85f9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc1c0de488190876b644cdaa41637 |
completed | March 7, 2026, 6:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b12ded5f9c8190a0de21b631d970b0 |
completed | March 11, 2026, 8:55 a.m. |
| NEDg | Description generation | batch_69b1316ee0708190bd27bb78f7f298d3 |
completed | March 11, 2026, 9:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b1d6729f848190a275638f074c1729 |
completed | March 11, 2026, 8:54 p.m. |
Created at: March 4, 2026, 7:48 p.m.