Triple
T4048278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bernalillo County |
E84120
|
entity |
| Predicate | bordersOn |
P224
|
FINISHED |
| Object |
Valencia County
Valencia County is a county in central New Mexico, United States, known for its mix of rural communities, agricultural areas, and proximity to the Albuquerque metropolitan region.
|
E442744
|
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: Valencia County | Statement: [Bernalillo County, bordersOn, Valencia County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Valencia County Context triple: [Bernalillo County, bordersOn, Valencia County]
-
A.
Suwannee County
Suwannee County is a rural county in northern Florida known for the Suwannee River, agriculture, and small-town communities.
-
B.
Lee County
Lee County is a coastal county on Florida’s Gulf Coast known for its beaches, barrier islands, and the city of Fort Myers.
-
C.
Lee County
Lee County is a county in northern Illinois known for its largely rural landscape, small towns, and agricultural economy.
-
D.
Lee County
Lee County is a county in eastern Alabama known for being home to the city of Auburn and Auburn University.
-
E.
Lauderdale County
Lauderdale County is a county in the northwestern part of Alabama, known for its seat in Florence and location along the Tennessee River.
- 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: Valencia County Triple: [Bernalillo County, bordersOn, Valencia County]
Generated description
Valencia County is a county in central New Mexico, United States, known for its mix of rural communities, agricultural areas, and proximity to the Albuquerque metropolitan region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Valencia County Target entity description: Valencia County is a county in central New Mexico, United States, known for its mix of rural communities, agricultural areas, and proximity to the Albuquerque metropolitan region.
-
A.
Suwannee County
Suwannee County is a rural county in northern Florida known for the Suwannee River, agriculture, and small-town communities.
-
B.
Lee County
Lee County is a coastal county on Florida’s Gulf Coast known for its beaches, barrier islands, and the city of Fort Myers.
-
C.
Lee County
Lee County is a county in eastern Alabama known for being home to the city of Auburn and Auburn University.
-
D.
Lee County
Lee County is a county in northern Illinois known for its largely rural landscape, small towns, and agricultural economy.
-
E.
Lauderdale County
Lauderdale County is a county in the northwestern part of Alabama, known for its seat in Florence and location along the Tennessee River.
- 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_69aed930bd5c819083e7dcc14fc44f69 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb81040481909b22e4c445ecae0f |
completed | March 9, 2026, 4:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b627b362508190905af163e9dbc1b4 |
completed | March 15, 2026, 3:29 a.m. |
| NEDg | Description generation | batch_69b628fe10908190978dd0361628f54f |
completed | March 15, 2026, 3:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b629ab52c881909f7fbef6f77b5bc4 |
completed | March 15, 2026, 3:38 a.m. |
Created at: March 9, 2026, 3:37 p.m.