Triple

T4425212
Position Surface form Disambiguated ID Type / Status
Subject PettingZoo E95191 entity
Predicate compatibleWith P203 FINISHED
Object Tianshou
Tianshou is a reinforcement learning library for PyTorch that provides modular, efficient tools and algorithms for training and evaluating RL agents.
 E438349 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: Tianshou | Statement: [PettingZoo, compatibleWith, Tianshou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tianshou
Context triple: [PettingZoo, compatibleWith, Tianshou]
  • A. Zheyuan
    Zheyuan is a given name most notably borne by the Chinese general and politician Song Zheyuan.
  • B. Zhiyuan
    Zhiyuan was a late 19th-century protected cruiser of the Qing Dynasty’s Beiyang Fleet, best known for its role and sinking in the First Sino-Japanese War.
  • C. Tsien
    Tsien is a Chinese surname borne by several notable figures in science and engineering, including biophysicist Richard Tsien.
  • D. Tiant
    Tiant is a surname most notably associated with Cuban former Major League Baseball pitcher Luis Tiant, known for his distinctive delivery and success with the Boston Red Sox in the 1970s.
  • E. Enbo
    Enbo is a given name most notably associated with Tang Enbo, a prominent Chinese Nationalist general during the Second Sino-Japanese War and Chinese Civil War.
  • 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: Tianshou
Triple: [PettingZoo, compatibleWith, Tianshou]
Generated description
Tianshou is a reinforcement learning library for PyTorch that provides modular, efficient tools and algorithms for training and evaluating RL agents.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tianshou
Target entity description: Tianshou is a reinforcement learning library for PyTorch that provides modular, efficient tools and algorithms for training and evaluating RL agents.
  • A. Zheyuan
    Zheyuan is a given name most notably borne by the Chinese general and politician Song Zheyuan.
  • B. Zhiyuan
    Zhiyuan was a late 19th-century protected cruiser of the Qing Dynasty’s Beiyang Fleet, best known for its role and sinking in the First Sino-Japanese War.
  • C. Tsien
    Tsien is a Chinese surname borne by several notable figures in science and engineering, including biophysicist Richard Tsien.
  • D. Tiant
    Tiant is a surname most notably associated with Cuban former Major League Baseball pitcher Luis Tiant, known for his distinctive delivery and success with the Boston Red Sox in the 1970s.
  • E. Enbo
    Enbo is a given name most notably associated with Tang Enbo, a prominent Chinese Nationalist general during the Second Sino-Japanese War and Chinese Civil War.
  • 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_69b3453c2a0c8190926b574c90766db9 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3554e40ec8190982acc0948da2f42 completed March 13, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69b5f633a69c8190b062c2a78b0f8319 completed March 14, 2026, 11:58 p.m.
NEDg Description generation batch_69b5f6bcfa0481909d07ffb2a975a350 completed March 15, 2026, 12:01 a.m.
NED2 Entity disambiguation (via description) batch_69b5f733c660819081c68dc3ec342e12 completed March 15, 2026, 12:02 a.m.
Created at: March 12, 2026, 11:30 p.m.