Triple

T29828043
Position Surface form Disambiguated ID Type / Status
Subject Time Walker E757439 entity
Predicate hasAlternativeTitle P39 FINISHED
Object Being from Another Planet
Being from Another Planet is a 1982 American science fiction horror film about a mysterious extraterrestrial entity that begins killing people after its discovery in a small town.
E1885769 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: Being from Another Planet | Statement: [Time Walker, hasAlternativeTitle, Being from Another Planet]
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: Being from Another Planet
Triple: [Time Walker, hasAlternativeTitle, Being from Another Planet]
Generated description
Being from Another Planet is a 1982 American science fiction horror film about a mysterious extraterrestrial entity that begins killing people after its discovery in a small town.

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_69f22457c84c8190a6d9f56bc74082a9 completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f675999c988190a5e220a4a2d45e50 completed May 2, 2026, 10:07 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26e600debc81909dccbd2ccc85eeac completed June 8, 2026, 3:55 p.m.
NEDg Description generation batch_6a26e75f1fcc8190afc9b3c16e7b79af completed June 8, 2026, 4:01 p.m.
NED2 Entity disambiguation (via description) batch_6a26e85c13b481909ec2805c294744d6 completed June 8, 2026, 4:05 p.m.
Created at: April 29, 2026, 5:32 p.m.