Triple
T8431885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lion Guard |
E199132
|
entity |
| Predicate | developer |
P73
|
FINISHED |
| Object | Ford Riley |
E732771
|
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: Ford Riley | Statement: [The Lion Guard, developer, Ford Riley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ford Riley Context triple: [The Lion Guard, developer, Ford Riley]
-
A.
Ford Riley
chosen
Ford Riley is an American television writer and producer best known for developing and executive producing Disney Junior’s animated series "The Lion Guard."
-
B.
Ryan O'Reily
Ryan O'Reily is a manipulative and street-smart Irish-American inmate known for his scheming and survival tactics in the HBO prison drama series "Oz."
-
C.
Chris Waller
Chris Waller is an American gymnastics coach and former UCLA gymnast who has served as the head coach of the UCLA Bruins women's gymnastics team.
-
D.
Jon Taffer
Jon Taffer is an American hospitality expert, entrepreneur, and television personality best known for transforming failing bars and nightclubs on the reality series "Bar Rescue."
-
E.
Jon DeVaan
Jon DeVaan is a longtime Microsoft engineering leader known for his key roles in developing and managing core Windows and Office technologies.
- 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_69ca8313c99081909a5c6d83b91de5b3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbd1a4876c81908d5a708bb1f35683 |
completed | March 31, 2026, 1:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce1d5a671c8190aebbe94e8838cddb |
completed | April 2, 2026, 7:40 a.m. |
Created at: March 30, 2026, 6:07 p.m.