Triple
T2300553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas Panhandle |
E51719
|
entity |
| Predicate | hasCounty |
P285
|
FINISHED |
| Object |
Scurry County
Scurry County is a county in western Texas known for its oil production, agriculture, and county seat of Snyder.
|
E306911
|
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: Scurry County | Statement: [Texas Panhandle, hasCounty, Scurry County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scurry County Context triple: [Texas Panhandle, hasCounty, Scurry County]
-
A.
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.
-
B.
Bandera County
Bandera County is a rural county in south-central Texas known for its scenic Hill Country landscapes and its reputation as the “Cowboy Capital of the World.”
-
C.
Eddy County
Eddy County is a county in southeastern New Mexico known for encompassing Carlsbad Caverns National Park and significant oil and gas production.
-
D.
McLennan County
McLennan County is a county in central Texas best known for encompassing the city of Waco, home to Baylor University.
-
E.
Harper County
Harper County is a sparsely populated rural county in northwestern Oklahoma known for its agricultural economy and small-town communities.
- 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: Scurry County Triple: [Texas Panhandle, hasCounty, Scurry County]
Generated description
Scurry County is a county in western Texas known for its oil production, agriculture, and county seat of Snyder.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Scurry County Target entity description: Scurry County is a county in western Texas known for its oil production, agriculture, and county seat of Snyder.
-
A.
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.
-
B.
Bandera County
Bandera County is a rural county in south-central Texas known for its scenic Hill Country landscapes and its reputation as the “Cowboy Capital of the World.”
-
C.
Eddy County
Eddy County is a county in southeastern New Mexico known for encompassing Carlsbad Caverns National Park and significant oil and gas production.
-
D.
McLennan County
McLennan County is a county in central Texas best known for encompassing the city of Waco, home to Baylor University.
-
E.
Harper County
Harper County is a sparsely populated rural county in northwestern Oklahoma known for its agricultural economy and small-town communities.
- 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_69b030fc819c8190a1bd9bba49760fec |
completed | March 10, 2026, 2:55 p.m. |
| NEDg | Description generation | batch_69b034d9140881909800f0f052fb4f83 |
completed | March 10, 2026, 3:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b035b102b081908b0d9f272ab9c1b1 |
completed | March 10, 2026, 3:16 p.m. |
Created at: March 4, 2026, 7:49 p.m.