Triple
T14832526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christopher B. Roberts |
E348742
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Auburn, Alabama |
E98092
|
NE FINISHED |
How this triple was built (2 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: Auburn, Alabama | Statement: [Christopher B. Roberts, workLocation, Auburn, Alabama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Auburn, Alabama Context triple: [Christopher B. Roberts, workLocation, Auburn, Alabama]
-
A.
Auburn, Georgia
Auburn, Georgia is a small city in Barrow and Gwinnett counties within the Atlanta metropolitan area.
-
B.
Auburn
chosen
Auburn is a city in eastern Alabama known for being home to Auburn University and its strong college-town atmosphere.
-
C.
Auburn
Auburn is a city in south-central Maine known for its historic architecture, role as a regional service and economic center, and its location along the Androscoggin River opposite Lewiston.
-
D.
Auburn
Auburn is a residential neighborhood located within the city of Cranston, Rhode Island.
-
E.
Auburn
Auburn is a small city in northeastern Indiana known for its automotive heritage and classic car museums.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d822ec69008190a9232caa68836872 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded075af0881908fb35a9e7ee46749 |
completed | April 14, 2026, 11:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff9976bc888190a050c2502d1f8e81 |
completed | May 9, 2026, 8:30 p.m. |
Created at: April 10, 2026, 1:52 a.m.