Triple

T19693139
Position Surface form Disambiguated ID Type / Status
Subject GoogLeNet E472885 entity
Predicate alsoKnownAs P39 FINISHED
Object Inception v1 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: Inception v1 | Statement: [GoogLeNet, alsoKnownAs, Inception v1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Inception v1
Context triple: [GoogLeNet, alsoKnownAs, Inception v1]
  • A. Inception v1 chosen
    Inception v1 is the original version of Google’s Inception deep convolutional neural network architecture, introduced for efficient and accurate image classification in the 2014 GoogLeNet model.
  • B. Inception
    Inception is a 2010 science fiction heist film directed by Christopher Nolan that explores dream manipulation and shared subconscious worlds.
  • C. Inception v4
    Inception v4 is an advanced deep convolutional neural network model for image recognition that refines and extends earlier Inception architectures to achieve higher accuracy and efficiency.
  • D. Inception v2
    Inception v2 is an improved version of Google’s Inception convolutional neural network architecture that enhances accuracy and efficiency through refined module design and training techniques.
  • E. Eames in Inception
    Eames in Inception is a charismatic and witty forger on Cobb’s team, known for his ability to impersonate others within dreams and for providing both comic relief and tactical ingenuity in the heist.
  • 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_69d8e515bef88190bc30781aea50537a completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e64211e5d481908358d922e0dca271 completed April 20, 2026, 3:11 p.m.
Created at: April 10, 2026, 1:46 p.m.