Triple

T13367652
Position Surface form Disambiguated ID Type / Status
Subject Abjad E318980 entity
Predicate vowelMarkingSystems P9875 FINISHED
Object niqqud in Hebrew LITERAL 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: niqqud in Hebrew | Statement: [Abjad, vowelMarkingSystems, niqqud in Hebrew]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: vowelMarkingSystems
Context triple: [Abjad, vowelMarkingSystems, niqqud in Hebrew]
  • A. hasVowelNotationSystem chosen
    Indicates that a writing or transcription system for a language includes a method for explicitly representing vowel sounds.
  • B. hasVowelSystem
    Indicates that an entity possesses a particular system or pattern of vowel sounds (a structured set of vowel phonemes or contrasts).
  • C. hasVowelHarmony
    Indicates that the phonological vowels in a word or morpheme conform to a systematic harmony pattern (e.g., all front or all back vowels) according to the language’s vowel harmony rules.
  • D. hasNasalVowels
    Indicates that the subject language or phonological system includes vowels that are produced with nasal airflow (nasalized vowels).
  • E. hasVowelLengthContrast
    Indicates that a language distinguishes word meanings based on differences in the length (duration) of vowel sounds.
  • F. None of above.

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_69d806b7bbac8190b85278c87fa7aff3 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dadcd652d48190a782fd1f57f34b6a completed April 11, 2026, 11:44 p.m.
PD Predicate disambiguation batch_69d9a02c9abc8190b328e7bae747bfc5 completed April 11, 2026, 1:13 a.m.
Created at: April 9, 2026, 9:32 p.m.