Triple
T20269642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shadowland |
E499059
|
entity |
| Predicate | associatedCharacter |
P12208
|
FINISHED |
| Object | Rafiki |
—
|
NE NERFINISHED |
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: [Shadowland, associatedCharacter, Rafiki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rafiki Context triple: [Shadowland, associatedCharacter, 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 lion prince who becomes king in Disney's animated film "The Lion King," known for his journey from guilt-ridden exile to courageous leader.
-
C.
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.
-
D.
Shenzi
Shenzi is a cunning hyena and one of the primary antagonists in Disney's "The Lion King," known for serving as a key henchman to Scar.
-
E.
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."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e675dc8e708190b840d687f134c9e8 |
completed | April 20, 2026, 6:52 p.m. |
Created at: April 11, 2026, 11:42 p.m.