Triple

T30674689
Position Surface form Disambiguated ID Type / Status
Subject Fifty-State Initiative E780879 entity
Predicate keyProponentInFiction P170002 FINISHED
Object Henry Peter Gyrich
Henry Peter Gyrich is a hardline U.S. government liaison and bureaucrat in Marvel Comics, best known for overseeing and regulating superhero activities, often in opposition to teams like the Avengers and the X-Men.
 E1925708 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: Henry Peter Gyrich | Statement: [Fifty-State Initiative, keyProponentInFiction, Henry Peter Gyrich]
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: Henry Peter Gyrich
Triple: [Fifty-State Initiative, keyProponentInFiction, Henry Peter Gyrich]
Generated description
Henry Peter Gyrich is a hardline U.S. government liaison and bureaucrat in Marvel Comics, best known for overseeing and regulating superhero activities, often in opposition to teams like the Avengers and the X-Men.

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_69f68b7fadd88190a17b92b09ddb6f11 completed May 2, 2026, 11:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a287101ee288190903a08a8de804607 completed June 9, 2026, 8:01 p.m.
NEDg Description generation batch_6a28732d855081908fb422b3d72e2f6d completed June 9, 2026, 8:10 p.m.
NED2 Entity disambiguation (via description) batch_6a2873cea9c881908f1b78aa918a1e49 completed June 9, 2026, 8:13 p.m.
Created at: April 29, 2026, 8:32 p.m.