Triple
T2300515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas Panhandle |
E51719
|
entity |
| Predicate | hasCounty |
P285
|
FINISHED |
| Object |
Gray County
Gray County is a rural county in the Texas Panhandle best known for its oil industry and county seat, Pampa.
|
E362169
|
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: Gray County | Statement: [Texas Panhandle, hasCounty, Gray County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gray County Context triple: [Texas Panhandle, hasCounty, Gray County]
-
A.
Burnet County
Burnet County is a central Texas county known for its scenic lakes, rolling hills, and outdoor recreation in the Texas Hill Country.
-
B.
McLennan County
McLennan County is a county in central Texas best known for encompassing the city of Waco, home to Baylor University.
-
C.
Gillespie County
Gillespie County is a central Texas county known for its scenic Hill Country landscapes, German heritage, and the historic town of Fredericksburg.
-
D.
Hastings County
Hastings County is a large, predominantly rural county in eastern Ontario, Canada, known for its forests, lakes, and outdoor recreation opportunities.
-
E.
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.”
- 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: Gray County Triple: [Texas Panhandle, hasCounty, Gray County]
Generated description
Gray County is a rural county in the Texas Panhandle best known for its oil industry and county seat, Pampa.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gray County Target entity description: Gray County is a rural county in the Texas Panhandle best known for its oil industry and county seat, Pampa.
-
A.
Burnet County
Burnet County is a central Texas county known for its scenic lakes, rolling hills, and outdoor recreation in the Texas Hill Country.
-
B.
McLennan County
McLennan County is a county in central Texas best known for encompassing the city of Waco, home to Baylor University.
-
C.
Gillespie County
Gillespie County is a central Texas county known for its scenic Hill Country landscapes, German heritage, and the historic town of Fredericksburg.
-
D.
Hastings County
Hastings County is a large, predominantly rural county in eastern Ontario, Canada, known for its forests, lakes, and outdoor recreation opportunities.
-
E.
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.”
- 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_69b3737db06481908b854eff532fce18 |
completed | March 13, 2026, 2:16 a.m. |
| NEDg | Description generation | batch_69b373f5a3108190aea3918190831f7e |
completed | March 13, 2026, 2:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3745e11c081909e8d9bc7551d9e81 |
completed | March 13, 2026, 2:20 a.m. |
Created at: March 4, 2026, 7:49 p.m.