Triple

T36756348
Position Surface form Disambiguated ID Type / Status
Subject Tales from the Crypt: Demon Knight E908061 entity
Predicate mainProtagonist P9202 FINISHED
Object Jeryline
Jeryline is the resilient, resourceful heroine of the horror film "Tales from the Crypt: Demon Knight," who becomes the key defender against demonic forces.
E2197300 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: Jeryline | Statement: [Tales from the Crypt: Demon Knight, mainProtagonist, Jeryline]
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: Jeryline
Triple: [Tales from the Crypt: Demon Knight, mainProtagonist, Jeryline]
Generated description
Jeryline is the resilient, resourceful heroine of the horror film "Tales from the Crypt: Demon Knight," who becomes the key defender against demonic forces.

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_69f76e779bec8190be0e1f87a131e0f4 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f7c97831f08190a2eda81dc6fce83b completed May 3, 2026, 10:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3c173af6308190a5c5bad4306fecfb completed June 24, 2026, 5:43 p.m.
NEDg Description generation batch_6a3c193a1fc881908332ab00462372e1 completed June 24, 2026, 5:51 p.m.
NED2 Entity disambiguation (via description) batch_6a3c57b8bd4c81909d429a799dac9063 completed June 24, 2026, 10:18 p.m.
Created at: May 3, 2026, 4:12 p.m.