Triple

T4371452
Position Surface form Disambiguated ID Type / Status
Subject Gina Bellman E98905 entity
Predicate notableWork P4 FINISHED
Object Jekyll E402805 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: Jekyll | Statement: [Gina Bellman, notableWork, Jekyll]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jekyll
Context triple: [Gina Bellman, notableWork, Jekyll]
  • A. Jekyll chosen
    Jekyll is a 2007 British television drama series created by Steven Moffat that offers a modern, suspenseful reimagining of Robert Louis Stevenson’s classic Dr. Jekyll and Mr. Hyde story.
  • B. Jekyll static site generator
    Jekyll is a popular open-source static site generator, written in Ruby, that transforms plain text files into simple, blog-aware websites without requiring a database.
  • C. MkDocs
    MkDocs is a static site generator geared toward building project documentation from Markdown files, typically configured with a simple YAML file and extensible through themes and plugins.
  • D. Rubinius
    Rubinius is an alternative Ruby implementation featuring a virtual machine and just-in-time compilation, designed for high performance and concurrency.
  • E. Markdown
    Markdown is a lightweight markup language that uses plain-text formatting syntax to create structured documents, most commonly used for README files, documentation, and web content.
  • 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_69b3454db3708190aeafd814413c4c3d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3521dffbc8190b9300a7f4f64bdc0 completed March 12, 2026, 11:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5e50bcc9481909b0b9d60198dce63 completed March 14, 2026, 10:45 p.m.
Created at: March 12, 2026, 11:17 p.m.