Triple
T4829720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Missouri Bootheel |
E107914
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Bollinger County
Bollinger County is a rural county in southeastern Missouri known for its rolling hills, forests, and small farming communities.
|
E484816
|
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: Bollinger County | Statement: [Missouri Bootheel, contains, Bollinger County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bollinger County Context triple: [Missouri Bootheel, contains, Bollinger County]
-
A.
Barton County
Barton County is a rural county in southwestern Missouri, United States, known as the birthplace of President Harry S. Truman.
-
B.
Webster County
Webster County is a rural county in southwestern Georgia known for its small population, agricultural landscape, and location within the state’s historic Black Belt region.
-
C.
Webster County
Webster County is a rural county in central West Virginia known for its mountainous terrain, outdoor recreation opportunities, and small, close-knit communities.
-
D.
Crane County
Crane County is a sparsely populated county in western Texas known for its oil production and rural desert landscape.
-
E.
Hutchinson County
Hutchinson County is a rural county in the Texas Panhandle known for its oil and gas production and small, closely knit 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: Bollinger County Triple: [Missouri Bootheel, contains, Bollinger County]
Generated description
Bollinger County is a rural county in southeastern Missouri known for its rolling hills, forests, and small farming communities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bollinger County Target entity description: Bollinger County is a rural county in southeastern Missouri known for its rolling hills, forests, and small farming communities.
-
A.
Barton County
Barton County is a rural county in southwestern Missouri, United States, known as the birthplace of President Harry S. Truman.
-
B.
Webster County
Webster County is a rural county in southwestern Georgia known for its small population, agricultural landscape, and location within the state’s historic Black Belt region.
-
C.
Webster County
Webster County is a rural county in central West Virginia known for its mountainous terrain, outdoor recreation opportunities, and small, close-knit communities.
-
D.
Crane County
Crane County is a sparsely populated county in western Texas known for its oil production and rural desert landscape.
-
E.
Hutchinson County
Hutchinson County is a rural county in the Texas Panhandle known for its oil and gas production and small, closely knit 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_69bd43fac8188190803f0327190621e4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6cc66c488190a49052e32411dc4b |
completed | March 20, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be89d3358c8190bb85ec1835a9f095 |
completed | March 21, 2026, 12:06 p.m. |
| NEDg | Description generation | batch_69be8b15879881908ec5ee997daca3cb |
completed | March 21, 2026, 12:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69be8bc2fc088190a13995f9230adea2 |
completed | March 21, 2026, 12:14 p.m. |
Created at: March 20, 2026, 1:24 p.m.