Triple
T16331132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regis Toomey |
E396553
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Regis |
E893798
|
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: Regis | Statement: [Regis Toomey, givenName, Regis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Regis Context triple: [Regis Toomey, givenName, Regis]
-
A.
Regis
Regis is an honorific term historically used in English to denote royal association, particularly in place names granted royal patronage.
-
B.
Regis
chosen
Regis is an electronic music producer and DJ known for his influential work in the techno and experimental music scenes.
-
C.
Doane
Doane is a surname most notably associated with William Croswell Doane, the first Episcopal Bishop of Albany and a prominent 19th-century American church leader.
-
D.
Loras
Loras is a skilled and charismatic knight from House Tyrell in the "A Song of Ice and Fire" novels and the "Game of Thrones" television series.
-
E.
Kogod
Kogod is the business school of American University in Washington, D.C., offering undergraduate and graduate programs in business and management.
- 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_69d87f255b788190a400eba031dd85d8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2c4dfd9688190a749e48ebc055baf |
completed | April 17, 2026, 11:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a002613c0e88190b91da8eba683c864 |
completed | May 10, 2026, 6:30 a.m. |
Created at: April 10, 2026, 5:07 a.m.