Triple

T2792697
Position Surface form Disambiguated ID Type / Status
Subject GNU Awk E61964 entity
Predicate shortName P43 FINISHED
Object gawk E61964 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: gawk | Statement: [GNU Awk, shortName, gawk]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: gawk
Context triple: [GNU Awk, shortName, gawk]
  • A. GNU Awk chosen
    GNU Awk is the GNU Project’s enhanced implementation of the AWK programming language, widely used for powerful text processing and data extraction in Unix-like environments.
  • B. GNU Grep
    GNU Grep is the GNU Project’s free, open-source implementation of the grep command-line utility used for fast text searching and pattern matching in files.
  • C. GNU Sed
    GNU Sed is the GNU Project’s free, open-source implementation of the stream editor `sed`, widely used for non-interactive text processing and transformation on Unix-like systems.
  • D. GAW
    GAW is a World Meteorological Organization program that coordinates global observations and analysis of atmospheric composition and related environmental changes.
  • E. FGw
    FGw is the Faculty of Humanities at the University of Amsterdam, encompassing disciplines such as languages, history, philosophy, arts, and cultural studies.
  • 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_69ab4b7f51d881908768300ebd2fbdae completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abddd107ac81908eb1a6946834eee3 completed March 7, 2026, 8:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc65ebe788190859012e930918b05 completed March 10, 2026, 7:21 a.m.
Created at: March 6, 2026, 9:58 p.m.