Triple
T1856290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bradley Stevens |
E41709
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Stevens |
E26256
|
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: Stevens | Statement: [Bradley Stevens, familyName, Stevens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stevens Context triple: [Bradley Stevens, familyName, Stevens]
-
A.
Stevens
chosen
Stevens is a common English-language surname borne by numerous notable individuals across fields such as sports, politics, arts, and academia.
-
B.
Rutledge
Rutledge is a small town in eastern Tennessee that serves as the county seat of Grainger County within the Knoxville metropolitan area.
-
C.
Stearns
Stearns is the middle name of the influential modernist poet and critic T. S. Eliot, whose full name is Thomas Stearns Eliot.
-
D.
Richardson
Richardson is a suburban city in the Dallas–Fort Worth metropolitan area known for its telecommunications industry and the University of Texas at Dallas.
-
E.
Tilden
Tilden is a given name and surname most notably associated with several prominent American figures in politics, sports, and the arts.
- 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_69a8864a83848190a4ec02721306c511 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb07e5ed48190a7b8858e2b355109 |
completed | March 7, 2026, 4:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69add1cb5b708190a0b89b157ea9da58 |
completed | March 8, 2026, 7:45 p.m. |
Created at: March 4, 2026, 7:33 p.m.