Triple

T4833507
Position Surface form Disambiguated ID Type / Status
Subject Intriguing properties of neural networks E108000 entity
Predicate author P4 FINISHED
Object Wojciech Zaremba E17410 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: Wojciech Zaremba | Statement: [Intriguing properties of neural networks, author, Wojciech Zaremba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wojciech Zaremba
Context triple: [Intriguing properties of neural networks, author, Wojciech Zaremba]
  • A. Wojciech Zaremba chosen
    Wojciech Zaremba is a Polish computer scientist and entrepreneur known for co-founding OpenAI and contributing to advances in artificial intelligence research.
  • B. Krzysztof Zaremba
    Krzysztof Zaremba is a Polish academic and professor who serves as the rector of the Warsaw University of Technology.
  • C. Piotr Sobociński
    Piotr Sobociński was a Polish cinematographer known for his visually expressive work on both European art films and major Hollywood productions.
  • D. Piotr Wysocki
    Piotr Wysocki was a Polish army officer and independence activist best known for initiating the November Uprising of 1830 against Russian rule.
  • E. Andrzej Bartkowiak
    Andrzej Bartkowiak is a Polish-American cinematographer and film director known for his work on major Hollywood action and comedy films.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69bd43fbe444819085cb970706ef73f7 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6cca88d88190a8ad6cf7856bdf69 completed March 20, 2026, 3:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf186311c88190a8fe34e497e4662b completed March 21, 2026, 10:14 p.m.
Created at: March 20, 2026, 1:25 p.m.