Triple
T3293844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Mississippi |
E69162
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Smith County
Smith County is a rural county in central Mississippi known for its small communities, agriculture, and pine forests.
|
E415329
|
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: Smith County | Statement: [Central Mississippi, contains, Smith County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Smith County Context triple: [Central Mississippi, contains, Smith County]
-
A.
Burnet County
Burnet County is a central Texas county known for its scenic lakes, rolling hills, and outdoor recreation in the Texas Hill Country.
-
B.
Hastings County
Hastings County is a large, predominantly rural county in eastern Ontario, Canada, known for its forests, lakes, and outdoor recreation opportunities.
-
C.
Cochran County
Cochran County is a sparsely populated rural county in far west Texas known for its agriculture and location along the New Mexico border.
-
D.
White County
White County is a county in northeastern Georgia known for its mountainous terrain, outdoor recreation, and proximity to historic gold-mining areas.
-
E.
Ellis County
Ellis County is a rural county in northwestern Oklahoma known for its agricultural economy and small, sparsely populated 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: Smith County Triple: [Central Mississippi, contains, Smith County]
Generated description
Smith County is a rural county in central Mississippi known for its small communities, agriculture, and pine forests.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Smith County Target entity description: Smith County is a rural county in central Mississippi known for its small communities, agriculture, and pine forests.
-
A.
Burnet County
Burnet County is a central Texas county known for its scenic lakes, rolling hills, and outdoor recreation in the Texas Hill Country.
-
B.
Hastings County
Hastings County is a large, predominantly rural county in eastern Ontario, Canada, known for its forests, lakes, and outdoor recreation opportunities.
-
C.
Cochran County
Cochran County is a sparsely populated rural county in far west Texas known for its agriculture and location along the New Mexico border.
-
D.
White County
White County is a county in northeastern Georgia known for its mountainous terrain, outdoor recreation, and proximity to historic gold-mining areas.
-
E.
Ellis County
Ellis County is a rural county in northwestern Oklahoma known for its agricultural economy and small, sparsely populated 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_69ad859d45748190b0742408c954b39f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb074f35081909dd3c8a09544b5f1 |
completed | March 8, 2026, 5:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5767842bc8190849c79a510160654 |
completed | March 14, 2026, 2:53 p.m. |
| NEDg | Description generation | batch_69b57729ca3881909be016ad92a65785 |
completed | March 14, 2026, 2:56 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5778dcdc08190aee087f6d54992dc |
completed | March 14, 2026, 2:58 p.m. |
Created at: March 8, 2026, 3:10 p.m.