Triple

T27782341
Position Surface form Disambiguated ID Type / Status
Subject Back to the Future: The Game E699366 entity
Predicate fourthEpisodeTitle P47963 FINISHED
Object Double Visions
Double Visions is the fourth episode of the episodic graphic adventure video game Back to the Future: The Game, continuing the time-traveling storyline inspired by the classic film trilogy.
E1789607 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: Double Visions | Statement: [Back to the Future: The Game, fourthEpisodeTitle, Double Visions]
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: Double Visions
Triple: [Back to the Future: The Game, fourthEpisodeTitle, Double Visions]
Generated description
Double Visions is the fourth episode of the episodic graphic adventure video game Back to the Future: The Game, continuing the time-traveling storyline inspired by the classic film trilogy.

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_69ef6a4b5a9081909c9111396c2be3d2 completed April 27, 2026, 1:53 p.m.
NER Named-entity recognition batch_69f637cf6d248190a86a85cfeba3719b completed May 2, 2026, 5:43 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12ecc3da7081909b6b4fcdf957842d completed May 24, 2026, 12:19 p.m.
NEDg Description generation batch_6a12ed678580819082d28135e3fcb818 completed May 24, 2026, 12:21 p.m.
NED2 Entity disambiguation (via description) batch_6a12eef15e2c819099088626fb78adce completed May 24, 2026, 12:28 p.m.
Created at: April 27, 2026, 5:11 p.m.