Triple
T13462748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Real County, Texas |
E311412
|
entity |
| Predicate | countySeat |
P383
|
FINISHED |
| Object |
Leakey, Texas
Leakey, Texas is a small town in the Texas Hill Country known as a gateway to outdoor recreation along the Frio River.
|
E1218559
|
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: Leakey, Texas | Statement: [Real County, Texas, countySeat, Leakey, Texas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leakey, Texas Context triple: [Real County, Texas, countySeat, Leakey, Texas]
-
A.
Leo, Texas
Leo, Texas is a small rural unincorporated community located in Cooke County in north-central Texas.
-
B.
Leonard, Texas
Leonard, Texas is a small rural city in North Texas known for its tight-knit community and annual Leonard Picnic celebration.
-
C.
Lockhart, Texas
Lockhart, Texas is a small Central Texas city renowned as the "Barbecue Capital of Texas" and a historic hub of the Austin–San Antonio region.
-
D.
Millican, Texas
Millican, Texas is a small rural community in east-central Texas known for its historic role as an early railroad town.
-
E.
Keene, Texas
Keene, Texas is a small city in North Texas known for its close-knit community and association with Southwestern Adventist University.
- 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: Leakey, Texas Triple: [Real County, Texas, countySeat, Leakey, Texas]
Generated description
Leakey, Texas is a small town in the Texas Hill Country known as a gateway to outdoor recreation along the Frio River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Leakey, Texas Target entity description: Leakey, Texas is a small town in the Texas Hill Country known as a gateway to outdoor recreation along the Frio River.
-
A.
Leo, Texas
Leo, Texas is a small rural unincorporated community located in Cooke County in north-central Texas.
-
B.
Leonard, Texas
Leonard, Texas is a small rural city in North Texas known for its tight-knit community and annual Leonard Picnic celebration.
-
C.
Lockhart, Texas
Lockhart, Texas is a small Central Texas city renowned as the "Barbecue Capital of Texas" and a historic hub of the Austin–San Antonio region.
-
D.
Millican, Texas
Millican, Texas is a small rural community in east-central Texas known for its historic role as an early railroad town.
-
E.
Keene, Texas
Keene, Texas is a small city in North Texas known for its close-knit community and association with Southwestern Adventist University.
- 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_69d806a938b8819097ec43a2229fc7f9 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf0d95fc81909d9f73d5315dc7b4 |
completed | April 12, 2026, 2:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006065741c8190ad4ceb6bd3d60f9f |
completed | May 10, 2026, 10:39 a.m. |
| NEDg | Description generation | batch_6a00620e66788190acccf2c8fce00943 |
completed | May 10, 2026, 10:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0062665d908190b8127b21edcad30e |
completed | May 10, 2026, 10:48 a.m. |
Created at: April 9, 2026, 9:41 p.m.