Triple
T4983247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Velma Melissa Rogers |
E111938
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Rogers |
E773
|
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: Rogers | Statement: [Velma Melissa Rogers, familyName, Rogers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rogers Context triple: [Velma Melissa Rogers, familyName, Rogers]
-
A.
Rogers
Rogers is a growing city in northwestern Arkansas known for its role in the Fayetteville–Springdale–Rogers metropolitan area and as a regional commercial and retail hub.
-
B.
Rogers
Rogers is a small suburban city in Minnesota known for its location northwest of Minneapolis and its blend of residential neighborhoods, light industry, and retail development.
-
C.
Rogers
chosen
Rogers is a common English-language surname borne by numerous notable individuals across fields such as science, politics, entertainment, and sports.
-
D.
Rogers
Rogers is a major Canadian communications and media company known for its wireless, cable, internet, and sports media services.
-
E.
Rogers & Wells
Rogers & Wells was a prominent New York-based law firm known for its corporate and international legal practice before merging into Clifford Chance in 2000.
- 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_69bd441adc208190b70a033a0741d01e |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd725489bc81908f660332e25f29cf |
completed | March 20, 2026, 4:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be8a13f5448190a49f914d1ba49a7a |
completed | March 21, 2026, 12:07 p.m. |
Created at: March 20, 2026, 1:33 p.m.