Triple
T20598559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Jungle Book (1967 film) |
E506112
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Bagheera |
—
|
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: Bagheera | Statement: [The Jungle Book (1967 film), featuresCharacter, Bagheera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bagheera Context triple: [The Jungle Book (1967 film), featuresCharacter, Bagheera]
-
A.
Bagheera
chosen
Bagheera is the wise and protective black panther who mentors and safeguards Mowgli in Disney’s live-action adaptation of The Jungle Book.
-
B.
Matra Bagheera
The Matra Bagheera is a 1970s French mid-engined sports car notable for its sleek wedge-shaped design and unusual three-abreast seating layout.
-
C.
Rambi
Rambi is a powerful rhinoceros companion in the Donkey Kong series who helps players charge through enemies and obstacles.
-
D.
Mufasa
Mufasa is the wise and noble lion king of the Pride Lands and father of Simba in Disney's The Lion King.
-
E.
The Tiger
The Tiger is the costumed tiger character that serves as the spirited athletic mascot for Clemson University’s sports teams.
- 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_69e0b4ba6ae88190af871e1f9522c704 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6aa1e251c8190926dafe1402eb63c |
completed | April 20, 2026, 10:35 p.m. |
Created at: April 16, 2026, 11:40 a.m.