Triple

T31656077
Position Surface form Disambiguated ID Type / Status
Subject Bender Bending Rodríguez E807861 entity
Predicate alias P39 FINISHED
Object Bending Unit 22
Bending Unit 22 is a robot model from the animated series Futurama, best known through the character Bender Bending Rodríguez, a hard-drinking, wisecracking bending robot.
 E1972475 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: Bending Unit 22 | Statement: [Bender Bending Rodríguez, alias, Bending Unit 22]
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: Bending Unit 22
Triple: [Bender Bending Rodríguez, alias, Bending Unit 22]
Generated description
Bending Unit 22 is a robot model from the animated series Futurama, best known through the character Bender Bending Rodríguez, a hard-drinking, wisecracking bending robot.

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_69f348daf95c81908b4c985b7ddcd0b3 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a95e38ac819096c03f2b9872260f completed May 3, 2026, 1:48 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b79e7084c8190824fc5cdfaa1fd35 completed June 12, 2026, 3:15 a.m.
NEDg Description generation batch_6a2b7e074d288190bba21c47f00449ec completed June 12, 2026, 3:33 a.m.
NED2 Entity disambiguation (via description) batch_6a2b7f2007a481908b2de9d8d7d81f52 completed June 12, 2026, 3:38 a.m.
Created at: April 30, 2026, 10:55 p.m.