Triple
T8237478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bill Rodgers |
E192446
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Rodgers |
E36063
|
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: Rodgers | Statement: [Bill Rodgers, familyName, Rodgers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rodgers Context triple: [Bill Rodgers, familyName, Rodgers]
-
A.
Rodgers
chosen
Rodgers is a surname of Scottish and Irish origin borne by various notable individuals across sports, entertainment, and other fields.
-
B.
Rodger
Rodger is the surname of Lord Rodger of Earlsferry, a prominent Scottish jurist who served as a Justice of the Supreme Court of the United Kingdom.
-
C.
Mat Rogers
Mat Rogers is an Australian former dual-code rugby international who played both rugby league and rugby union at elite levels, including representing Australia and starring in the NRL.
-
D.
Ab Rogers
Ab Rogers is a British designer and architect known for his innovative, playful interior and exhibition designs and for founding Ab Rogers Design.
-
E.
Rodger Dodger
Rodger Dodger is a 2002 dark comedy-drama film about a cynical New York advertising executive who spends a night trying to teach his teenage nephew how to seduce women.
- 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_69ca82dc8f148190a2c75a98501a7b91 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb783929c081909db1182947755bae |
completed | March 31, 2026, 7:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd3504e6ac8190b4cb12c80a7e7fc0 |
completed | April 1, 2026, 3:08 p.m. |
Created at: March 30, 2026, 5:47 p.m.