Triple
T17531211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hidalgo County |
E426936
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Donna, Texas |
—
|
NE NERFINISHED |
How this triple was built (2 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: Donna, Texas | Statement: [Hidalgo County, contains, Donna, Texas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Donna, Texas Context triple: [Hidalgo County, contains, Donna, Texas]
-
A.
Donna, Texas
chosen
Donna, Texas is a small city in the Rio Grande Valley of South Texas, known for its agricultural roots and proximity to the U.S.–Mexico border.
-
B.
Donie, Texas
Donie, Texas is an unincorporated rural community located in Freestone County in east-central Texas.
-
C.
O'Donnell, Texas
O'Donnell, Texas is a small rural community in West Texas known historically for its agricultural roots and tight-knit local population.
-
D.
DeSoto, Texas
DeSoto, Texas is a suburban city in the Dallas–Fort Worth metropolitan area known for its residential communities and proximity to Dallas.
-
E.
Lorena, Texas
Lorena, Texas is a small city in Central Texas that functions as a suburban community within the Waco metropolitan area.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e453688950819098162d853cd2674e |
completed | April 19, 2026, 4 a.m. |
Created at: April 10, 2026, 5:49 a.m.