Triple

T30581467
Position Surface form Disambiguated ID Type / Status
Subject N'Jadaka E778391 entity
Predicate filmVersionName P50342 FINISHED
Object Erik Stevens
Erik Stevens, also known as Killmonger, is a formidable and vengeful antagonist in Marvel's "Black Panther" film, distinguished by his elite military training and challenge to Wakanda's traditions.
E1936473 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: Erik Stevens | Statement: [N'Jadaka, filmVersionName, Erik Stevens]
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: Erik Stevens
Triple: [N'Jadaka, filmVersionName, Erik Stevens]
Generated description
Erik Stevens, also known as Killmonger, is a formidable and vengeful antagonist in Marvel's "Black Panther" film, distinguished by his elite military training and challenge to Wakanda's traditions.

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_69f224a04b248190b0ca443ec86207b8 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f6894261048190aa19ffe41a34413c completed May 2, 2026, 11:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a28c7b3b254819097d00c24b4406863 completed June 10, 2026, 2:10 a.m.
NEDg Description generation batch_6a28d473e0ec81908011bf6f53de3cdf completed June 10, 2026, 3:05 a.m.
NED2 Entity disambiguation (via description) batch_6a28d48d59f48190a9a7b48917b9c5ad completed June 10, 2026, 3:05 a.m.
Created at: April 29, 2026, 8:23 p.m.