Triple

T2900972
Position Surface form Disambiguated ID Type / Status
Subject Greyhound E62651 entity
Predicate director P255 FINISHED
Object Aaron Schneider
Aaron Schneider is an American film director and cinematographer best known for directing the critically acclaimed drama "Get Low."
 E308448 NE FINISHED

How this triple was built (4 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: Aaron Schneider | Statement: [Greyhound, director, Aaron Schneider]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Aaron Schneider
Context triple: [Greyhound, director, Aaron Schneider]
  • A. Mike Nolan
    Mike Nolan is a name shared by several notable individuals, including a British singer from the pop group Bucks Fizz and various sports coaches and players.
  • B. Mark Daboll
    Mark Daboll is a member of the Daboll family, related to NFL head coach Brian Daboll.
  • C. Mike Schuster
    Mike Schuster is a computer scientist known for his contributions to deep learning and sequence modeling, including work on neural machine translation at Google.
  • D. Ken Schretzmann
    Ken Schretzmann is a film editor known for his work on major animated features, including Guillermo del Toro's stop-motion adaptation of Pinocchio.
  • E. Shawn Ryan
    Shawn Ryan is an American television producer and writer best known for creating the acclaimed crime drama series "The Shield" and producing several other notable TV shows.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Aaron Schneider
Triple: [Greyhound, director, Aaron Schneider]
Generated description
Aaron Schneider is an American film director and cinematographer best known for directing the critically acclaimed drama "Get Low."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Aaron Schneider
Target entity description: Aaron Schneider is an American film director and cinematographer best known for directing the critically acclaimed drama "Get Low."
  • A. Mike Nolan
    Mike Nolan is a name shared by several notable individuals, including a British singer from the pop group Bucks Fizz and various sports coaches and players.
  • B. Mark Daboll
    Mark Daboll is a member of the Daboll family, related to NFL head coach Brian Daboll.
  • C. Mike Schuster
    Mike Schuster is a computer scientist known for his contributions to deep learning and sequence modeling, including work on neural machine translation at Google.
  • D. Ken Schretzmann
    Ken Schretzmann is a film editor known for his work on major animated features, including Guillermo del Toro's stop-motion adaptation of Pinocchio.
  • E. Shawn Ryan
    Shawn Ryan is an American television producer and writer best known for creating the acclaimed crime drama series "The Shield" and producing several other notable TV shows.
  • F. None of above. chosen

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_69ab4c3e070c8190b78d3d2c005876dd completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abe0b261c081909b66b21520b4731b completed March 7, 2026, 8:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69b0318f2c908190aa10aa93f8fb139c completed March 10, 2026, 2:58 p.m.
NEDg Description generation batch_69b0405be9008190b1a264bc20e794c6 completed March 10, 2026, 4:01 p.m.
NED2 Entity disambiguation (via description) batch_69b044ad7cd48190b4a3ae143131616e completed March 10, 2026, 4:19 p.m.
Created at: March 6, 2026, 10:10 p.m.