Triple

T30673683
Position Surface form Disambiguated ID Type / Status
Subject Azzuri the Wise E780857 entity
Predicate occupation P3 FINISHED
Object King of Wakanda
The King of Wakanda is the sovereign ruler of the technologically advanced, vibranium-rich African nation in the Marvel universe, often bearing the mantle of the Black Panther.
E1937331 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: King of Wakanda | Statement: [Azzuri the Wise, occupation, King of Wakanda]
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: King of Wakanda
Triple: [Azzuri the Wise, occupation, King of Wakanda]
Generated description
The King of Wakanda is the sovereign ruler of the technologically advanced, vibranium-rich African nation in the Marvel universe, often bearing the mantle of the Black Panther.

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_69f224a7fc208190a07d6d3879b31640 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f68b16d9048190a828904d096052dd completed May 2, 2026, 11:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a28e447f6888190a992111b2f28897d completed June 10, 2026, 4:12 a.m.
NEDg Description generation batch_6a28e4de85808190b97669b0a2272d09 completed June 10, 2026, 4:15 a.m.
NED2 Entity disambiguation (via description) batch_6a28e543219081909141e4aec28cff29 completed June 10, 2026, 4:17 a.m.
Created at: April 29, 2026, 8:32 p.m.