Triple

T25949044
Position Surface form Disambiguated ID Type / Status
Subject Joe Flanigan E653920 entity
Predicate notableWork P4 FINISHED
Object Ferocious Planet
Ferocious Planet is a 2011 science fiction television film in which a government experiment accidentally transports a group of people to a dangerous parallel world filled with deadly creatures.
E1699643 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: Ferocious Planet | Statement: [Joe Flanigan, notableWork, Ferocious 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: Ferocious Planet
Triple: [Joe Flanigan, notableWork, Ferocious Planet]
Generated description
Ferocious Planet is a 2011 science fiction television film in which a government experiment accidentally transports a group of people to a dangerous parallel world filled with deadly creatures.

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_69e7ab40ac788190a771bc499eb1ae5f completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f604938afc8190941e50a9e3911931 completed May 2, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10ece6908881908246050a44a5b885 completed May 22, 2026, 11:55 p.m.
NEDg Description generation batch_6a10ef4b63d4819095fd0f0538e3f3f3 completed May 23, 2026, 12:05 a.m.
NED2 Entity disambiguation (via description) batch_6a10efac702c819082f0518a40c89a40 completed May 23, 2026, 12:07 a.m.
Created at: April 22, 2026, 8:43 a.m.