Triple

T28638336
Position Surface form Disambiguated ID Type / Status
Subject Charlton Comics E724851 entity
Predicate notableCharacterPublisherOf P117210 FINISHED
Object Nightshade
Nightshade is a comic book superheroine known for her shadow-based powers and espionage background, originally appearing in Charlton Comics before being integrated into DC Comics.
E1458767 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: Nightshade | Statement: [Charlton Comics, notableCharacterPublisherOf, Nightshade]
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: Nightshade
Triple: [Charlton Comics, notableCharacterPublisherOf, Nightshade]
Generated description
Nightshade is a comic book superheroine known for her shadow-based powers and espionage background, originally appearing in Charlton Comics before being integrated into DC Comics.

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_69f01d8328c48190bc0e5f9b9b848582 completed April 28, 2026, 2:37 a.m.
NER Named-entity recognition batch_6a0148e3667081908c701748aa95feb4 completed May 11, 2026, 3:11 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1cc37fe42c81909e7af258cbc598ce completed May 31, 2026, 11:25 p.m.
NEDg Description generation batch_6a1cc4a74dec8190ab3ce653f778ec13 completed May 31, 2026, 11:30 p.m.
NED2 Entity disambiguation (via description) batch_6a1cc544e60081908682c3750e6ac83d completed May 31, 2026, 11:33 p.m.
Created at: April 28, 2026, 4:42 a.m.