Triple

T36540759

Position	Surface form	Disambiguated ID	Type / Status
Subject	Heroes Reborn	`E900713`	entity
Predicate	hasMainCharacter	`P1183`	FINISHED
Object	Erica Kravid Erica Kravid is a central antagonist in the TV miniseries "Heroes Reborn," known as the powerful and calculating CEO of Renautas who seeks to control the fate of evolved humans.	`E2198578`	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: Erica Kravid | Statement: [Heroes Reborn, hasMainCharacter, Erica Kravid]

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: Erica Kravid
Triple: [Heroes Reborn, hasMainCharacter, Erica Kravid]

Generated description

Erica Kravid is a central antagonist in the TV miniseries "Heroes Reborn," known as the powerful and calculating CEO of Renautas who seeks to control the fate of evolved humans.

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_69f76e5fbb388190b70c4c15573c8143`	completed	May 3, 2026, 3:48 p.m.
NER	Named-entity recognition	`batch_69f7c241d5948190ab1e92d1f0867dc8`	completed	May 3, 2026, 9:46 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a3d1781558c8190a386908f82d40585`	completed	June 25, 2026, 11:56 a.m.
NEDg	Description generation	`batch_6a3d1edebb8c81908fd5efffd31ee426`	completed	June 25, 2026, 12:28 p.m.
NED2	Entity disambiguation (via description)	`batch_6a3d66d6a75c8190af8772adb65e6d0b`	completed	June 25, 2026, 5:35 p.m.

Created at: May 3, 2026, 4:11 p.m.