Triple

T26895564
Position Surface form Disambiguated ID Type / Status
Subject Kiara and Kovu E677888 entity
Predicate fandom P8696 FINISHED
Object The Lion King fandom
The Lion King fandom is a passionate global community of fans who celebrate and create content around Disney’s The Lion King franchise, including its characters, stories, music, and spin-offs.
E1748434 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: The Lion King fandom | Statement: [Kiara and Kovu, fandom, The Lion King fandom]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: The Lion King fandom
Triple: [Kiara and Kovu, fandom, The Lion King fandom]
Generated description
The Lion King fandom is a passionate global community of fans who celebrate and create content around Disney’s The Lion King franchise, including its characters, stories, music, and spin-offs.

Provenance (5 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_69eee9befee48190a26f214faa867be7 completed April 27, 2026, 4:44 a.m.
NER Named-entity recognition batch_69f61fa9a1ec8190bc4063656815a1c5 completed May 2, 2026, 4 p.m.
NED1 Entity disambiguation (via context triple) batch_6a121ea4a1148190b6d2b19c52a513c6 completed May 23, 2026, 9:39 p.m.
NEDg Description generation batch_6a12204c2da88190b71d8ea3247650d3 completed May 23, 2026, 9:46 p.m.
NED2 Entity disambiguation (via description) batch_6a12210f8ae881908b5f0a9fceb7bcf7 completed May 23, 2026, 9:50 p.m.
Created at: April 27, 2026, 5:47 a.m.