Triple

T25194752
Position Surface form Disambiguated ID Type / Status
Subject Marvel Zombies E630970 entity
Predicate mainCharacter P1183 FINISHED
Object Zombie Colonel America
Zombie Colonel America is the undead, alternate-universe version of Captain America featured prominently in Marvel's horror-themed Marvel Zombies comic series.
E1668704 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: Zombie Colonel America | Statement: [Marvel Zombies, mainCharacter, Zombie Colonel America]
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: Zombie Colonel America
Triple: [Marvel Zombies, mainCharacter, Zombie Colonel America]
Generated description
Zombie Colonel America is the undead, alternate-universe version of Captain America featured prominently in Marvel's horror-themed Marvel Zombies comic series.

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_69e75a8a6d088190ba1e82a4345225e7 completed April 21, 2026, 11:07 a.m.
NER Named-entity recognition batch_69f46e12ea808190b08610a16810bc3a completed May 1, 2026, 9:10 a.m.
NED1 Entity disambiguation (via context triple) batch_6a105d2882788190824154d643158457 completed May 22, 2026, 1:42 p.m.
NEDg Description generation batch_6a105e17b4708190bceee2e6c3f4f3a7 completed May 22, 2026, 1:45 p.m.
NED2 Entity disambiguation (via description) batch_6a105fa381408190b9343fb060d29374 completed May 22, 2026, 1:52 p.m.
Created at: April 21, 2026, 12:46 p.m.