Triple

T35469796
Position Surface form Disambiguated ID Type / Status
Subject Evil League of Evil E1025175 entity
Predicate hasMember P10 FINISHED
Object Fake Thomas Jefferson
Fake Thomas Jefferson is a fictional supervillain persona associated with the Evil League of Evil from Joss Whedon’s web musical "Dr. Horrible’s Sing-Along Blog."
E2143234 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: Fake Thomas Jefferson | Statement: [Evil League of Evil, hasMember, Fake Thomas Jefferson]
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: Fake Thomas Jefferson
Triple: [Evil League of Evil, hasMember, Fake Thomas Jefferson]
Generated description
Fake Thomas Jefferson is a fictional supervillain persona associated with the Evil League of Evil from Joss Whedon’s web musical "Dr. Horrible’s Sing-Along Blog."

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_69f76dfa20d0819089585dc2cf653aea completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f796afecd48190bf2aaff09c92a065 completed May 3, 2026, 6:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a384036214c8190b3fb10c5d3a84fcf completed June 21, 2026, 7:49 p.m.
NEDg Description generation batch_6a3841bfab048190890a5321c0899cb2 completed June 21, 2026, 7:55 p.m.
NED2 Entity disambiguation (via description) batch_6a38459abc9c8190b99780678317fb86 completed June 21, 2026, 8:12 p.m.
Created at: May 3, 2026, 4:04 p.m.