Triple
T7630437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Saba, Texas |
E172746
|
entity |
| Predicate | county |
P75
|
FINISHED |
| Object |
San Saba County
San Saba County is a rural county in central Texas known for its ranching, pecan production, and small-town communities.
|
E689201
|
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: San Saba County | Statement: [San Saba, Texas, county, San Saba County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Saba County Context triple: [San Saba, Texas, county, San Saba County]
-
A.
Mayes County
Mayes County is a county in northeastern Oklahoma known for its mix of small towns, agricultural areas, and recreational lakes.
-
B.
Fisher County
Fisher County is a rural county in west-central Texas known for its agricultural economy and small, sparsely populated communities.
-
C.
Harding County
Harding County is a sparsely populated rural county in northeastern New Mexico known for its ranching landscape and wide-open high plains.
-
D.
Briscoe County
Briscoe County is a rural county in the Texas Panhandle known for its agricultural economy and proximity to the scenic Caprock Canyons region.
-
E.
Van Zandt County
Van Zandt County is a rural county in northeastern Texas known for its agricultural heritage 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: San Saba County Triple: [San Saba, Texas, county, San Saba County]
Generated description
San Saba County is a rural county in central Texas known for its ranching, pecan production, and small-town communities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: San Saba County Target entity description: San Saba County is a rural county in central Texas known for its ranching, pecan production, and small-town communities.
-
A.
Mayes County
Mayes County is a county in northeastern Oklahoma known for its mix of small towns, agricultural areas, and recreational lakes.
-
B.
Fisher County
Fisher County is a rural county in west-central Texas known for its agricultural economy and small, sparsely populated communities.
-
C.
Harding County
Harding County is a sparsely populated rural county in northeastern New Mexico known for its ranching landscape and wide-open high plains.
-
D.
Briscoe County
Briscoe County is a rural county in the Texas Panhandle known for its agricultural economy and proximity to the scenic Caprock Canyons region.
-
E.
Van Zandt County
Van Zandt County is a rural county in northeastern Texas known for its agricultural heritage 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_69c699517e348190bd3348b6889200f2 |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6fa85c57c8190acfd33e0c890c2f9 |
completed | March 27, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8f30833588190a7217fdf160f2d49 |
completed | March 29, 2026, 9:38 a.m. |
| NEDg | Description generation | batch_69c8f392edd8819089852a9fd71cc970 |
completed | March 29, 2026, 9:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8f48bdf1c8190aab34120be7b08e1 |
completed | March 29, 2026, 9:44 a.m. |
Created at: March 27, 2026, 3:56 p.m.