Triple
T2300556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas Panhandle |
E51719
|
entity |
| Predicate | hasCounty |
P285
|
FINISHED |
| Object |
Nolan County
Nolan County is a county in west-central Texas known for its wind energy production and county seat, Sweetwater.
|
E376944
|
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: Nolan County | Statement: [Texas Panhandle, hasCounty, Nolan County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nolan County Context triple: [Texas Panhandle, hasCounty, Nolan County]
-
A.
McLennan County
McLennan County is a county in central Texas best known for encompassing the city of Waco, home to Baylor University.
-
B.
Lamb County
Lamb County is a rural county in northwestern Texas known for its agriculture-based economy and small communities on the High Plains.
-
C.
Gray County
Gray County is a rural county in the Texas Panhandle best known for its oil industry and county seat, Pampa.
-
D.
Dallam County
Dallam County is a sparsely populated rural county in the far northwestern corner of the Texas Panhandle, known for its agricultural economy and wide-open High Plains landscape.
-
E.
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.
- 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: Nolan County Triple: [Texas Panhandle, hasCounty, Nolan County]
Generated description
Nolan County is a county in west-central Texas known for its wind energy production and county seat, Sweetwater.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nolan County Target entity description: Nolan County is a county in west-central Texas known for its wind energy production and county seat, Sweetwater.
-
A.
McLennan County
McLennan County is a county in central Texas best known for encompassing the city of Waco, home to Baylor University.
-
B.
Lamb County
Lamb County is a rural county in northwestern Texas known for its agriculture-based economy and small communities on the High Plains.
-
C.
Gray County
Gray County is a rural county in the Texas Panhandle best known for its oil industry and county seat, Pampa.
-
D.
Dallam County
Dallam County is a sparsely populated rural county in the far northwestern corner of the Texas Panhandle, known for its agricultural economy and wide-open High Plains landscape.
-
E.
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.
- 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_69b48810202c8190a2a8d5ae849b6d9e |
completed | March 13, 2026, 9:56 p.m. |
| NEDg | Description generation | batch_69b48a9a55fc8190bc7de7c2c1e9bf76 |
completed | March 13, 2026, 10:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4a3d9377481909bc0392a1176601b |
completed | March 13, 2026, 11:55 p.m. |
Created at: March 4, 2026, 7:49 p.m.