Triple
T2300531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas Panhandle |
E51719
|
entity |
| Predicate | hasCounty |
P285
|
FINISHED |
| Object |
Swisher County
Swisher County is a rural county in the Texas Panhandle known for its agriculture-based economy and small-town communities.
|
E370618
|
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: Swisher County | Statement: [Texas Panhandle, hasCounty, Swisher County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Swisher County Context triple: [Texas Panhandle, hasCounty, Swisher County]
-
A.
Hansford County
Hansford County is a rural county in the far northern Texas Panhandle known for its agriculture-based economy and small, sparsely populated communities.
-
B.
Scurry County
Scurry County is a county in western Texas known for its oil production, agriculture, and county seat of Snyder.
-
C.
Kerr County
Kerr County is a rural county in central Texas known for its scenic Hill Country landscapes, outdoor recreation, and the county seat of Kerrville.
-
D.
Gray County
Gray County is a rural county in the Texas Panhandle best known for its oil industry and county seat, Pampa.
-
E.
Burnet County
Burnet County is a central Texas county known for its scenic lakes, rolling hills, and outdoor recreation in the Texas Hill Country.
- 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: Swisher County Triple: [Texas Panhandle, hasCounty, Swisher County]
Generated description
Swisher County is a rural county in the Texas Panhandle known for its agriculture-based economy and small-town communities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Swisher County Target entity description: Swisher County is a rural county in the Texas Panhandle known for its agriculture-based economy and small-town communities.
-
A.
Hansford County
Hansford County is a rural county in the far northern Texas Panhandle known for its agriculture-based economy and small, sparsely populated communities.
-
B.
Scurry County
Scurry County is a county in western Texas known for its oil production, agriculture, and county seat of Snyder.
-
C.
Kerr County
Kerr County is a rural county in central Texas known for its scenic Hill Country landscapes, outdoor recreation, and the county seat of Kerrville.
-
D.
Gray County
Gray County is a rural county in the Texas Panhandle best known for its oil industry and county seat, Pampa.
-
E.
Burnet County
Burnet County is a central Texas county known for its scenic lakes, rolling hills, and outdoor recreation in the Texas Hill Country.
- 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_69a88b0a9f248190bcff941463d8f65a |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abc5edc1348190a4d84606b1310711 |
completed | March 7, 2026, 6:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bb56c2e081909168c0fef26bff44 |
completed | March 13, 2026, 7:23 a.m. |
| NEDg | Description generation | batch_69b3bf4336b08190ba74f0b9d5bf399e |
completed | March 13, 2026, 7:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3fa663e508190af580cf2de425fc5 |
completed | March 13, 2026, 11:52 a.m. |
Created at: March 4, 2026, 7:49 p.m.