Triple

T17694171
Position Surface form Disambiguated ID Type / Status
Subject Hindsight Policy Gradients E441117 entity
Predicate introducedBy P513 FINISHED
Object Filip Wolski NE NERFINISHED

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: Filip Wolski | Statement: [Hindsight Policy Gradients, introducedBy, Filip Wolski]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Filip Wolski
Context triple: [Hindsight Policy Gradients, introducedBy, Filip Wolski]
  • A. Filip Wolski chosen
    Filip Wolski is a machine learning researcher known for his work at OpenAI, including contributions to reinforcement learning methods such as Proximal Policy Optimization (PPO).
  • B. Piotr Wolski
    Piotr Wolski is a researcher known for co-authoring scientific work with machine learning scientist Marcin Andrychowicz.
  • C. Dariusz Wolski
    Dariusz Wolski is a Polish cinematographer known for his work on major films such as the "Pirates of the Caribbean" series and collaborations with directors like Ridley Scott.
  • D. Krzysztof Olszewski
    Krzysztof Olszewski is a Polish entrepreneur and engineer best known as the founder of the bus and trolleybus manufacturer Solaris Bus & Coach.
  • E. Pawel Pawlowski
    Pawel Pawlowski is a physicist recognized for his significant contributions to nuclear science, honored as a laureate of the prestigious Lise Meitner Prize.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9e940b081908b862bb0e6e89b0d completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e4715485d88190b9b6f347ff85d7c7 completed April 19, 2026, 6:08 a.m.
Created at: April 10, 2026, 10:04 a.m.