Triple
T10761952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oldham County |
E253849
|
entity |
| Predicate | seat |
P75
|
FINISHED |
| Object |
Vega, Texas
Vega, Texas is a small city in the Texas Panhandle that serves as the administrative and commercial hub of Oldham County.
|
E884647
|
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: Vega, Texas | Statement: [Oldham County, seat, Vega, Texas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vega, Texas Context triple: [Oldham County, seat, Vega, Texas]
-
A.
Velasco, Texas
Velasco, Texas was a historic Gulf Coast port town that played a key role in early Texas history, including as the site where treaties ending the Texas Revolution were signed.
-
B.
Venus, Texas
Venus, Texas is a small town in Johnson and Ellis counties within the Dallas–Fort Worth metropolitan area.
-
C.
Victoria, Texas
Victoria, Texas is a small city in southeastern Texas that serves as a regional hub for commerce, healthcare, and legal services along the Gulf Coast.
-
D.
Grapevine, Texas
Grapevine, Texas is a suburban city in North Texas known for its historic downtown, wineries, and proximity to Dallas/Fort Worth International Airport.
-
E.
Van, Texas
Van, Texas is a small city in East Texas known historically for its oil production and close-knit rural community.
- 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: Vega, Texas Triple: [Oldham County, seat, Vega, Texas]
Generated description
Vega, Texas is a small city in the Texas Panhandle that serves as the administrative and commercial hub of Oldham County.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vega, Texas Target entity description: Vega, Texas is a small city in the Texas Panhandle that serves as the administrative and commercial hub of Oldham County.
-
A.
Velasco, Texas
Velasco, Texas was a historic Gulf Coast port town that played a key role in early Texas history, including as the site where treaties ending the Texas Revolution were signed.
-
B.
Venus, Texas
Venus, Texas is a small town in Johnson and Ellis counties within the Dallas–Fort Worth metropolitan area.
-
C.
Victoria, Texas
Victoria, Texas is a small city in southeastern Texas that serves as a regional hub for commerce, healthcare, and legal services along the Gulf Coast.
-
D.
Grapevine, Texas
Grapevine, Texas is a suburban city in North Texas known for its historic downtown, wineries, and proximity to Dallas/Fort Worth International Airport.
-
E.
Van, Texas
Van, Texas is a small city in East Texas known historically for its oil production and close-knit rural community.
- 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_69d6aa5f54f4819082d0bbcb6f8797e6 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d731a230ac8190920439076aaeb91e |
completed | April 9, 2026, 4:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de2351db9c8190983ac834ea069fb4 |
completed | April 14, 2026, 11:21 a.m. |
| NEDg | Description generation | batch_69de271ee56c81908d2f690f31c2d2db |
completed | April 14, 2026, 11:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69de2dff4a048190823c8b5f1f7ea548 |
completed | April 14, 2026, 12:07 p.m. |
Created at: April 8, 2026, 9:16 p.m.