Triple
T7643652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simba |
E173068
|
entity |
| Predicate | mentor |
P3665
|
FINISHED |
| Object | Rafiki |
E499058
|
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: Rafiki | Statement: [Simba, mentor, Rafiki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rafiki Context triple: [Simba, mentor, Rafiki]
-
A.
Rafiki
chosen
Rafiki is a wise, mystical mandrill who serves as a spiritual guide and shaman-like figure in Disney’s The Lion King.
-
B.
Simba
Simba is the stage name of Tanzanian singer and songwriter Diamond Platnumz, a leading figure in contemporary Bongo Flava and East African pop music.
-
C.
Simba
Simba is the lion prince who becomes king in Disney's animated film "The Lion King," known for his journey from guilt-ridden exile to courageous leader.
-
D.
Jobu Tupaki
Jobu Tupaki is the chaotic, multiverse-hopping alter ego of Joy Wang who serves as the film’s primary antagonist in the sci-fi action movie "Everything Everywhere All at Once."
-
E.
Banzi
Banzi is a town in the Basilicata region of southern Italy, known as the modern site near the ancient Lucanian city of Bantia.
- 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_69c6995360188190968ee57b72a1627f |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6faef96908190a7724b204f9d8c9e |
completed | March 27, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c870d510f08190ad7706f582e8c1a0 |
completed | March 29, 2026, 12:22 a.m. |
Created at: March 27, 2026, 3:58 p.m.