Triple

T25104785
Position Surface form Disambiguated ID Type / Status
Subject Basic Law: Israel Lands E628837 entity
Predicate percentageOfLandCovered P2022 FINISHED
Object most of the land area of Israel 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: most of the land area of Israel | Statement: [Basic Law: Israel Lands, percentageOfLandCovered, most of the land area of Israel]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: percentageOfLandCovered
Context triple: [Basic Law: Israel Lands, percentageOfLandCovered, most of the land area of Israel]
  • A. vegetationCoverage
    Indicates the extent or proportion of an area that is covered by vegetation.
  • B. hasLandCoverage chosen
    Indicates that a specified area or region is covered or occupied by a particular type of land surface or land use.
  • C. forestCoverCharacteristic
    Indicates a relationship where a forested area possesses a specific attribute or quality related to its tree or vegetation cover.
  • D. percentageOfLandInNationalPark
    Indicates the proportion of a given area’s total land that lies within designated national park boundaries.
  • E. areaWaterPercentage
    Indicates the proportion of an entity’s total area that is covered by water, typically expressed as a percentage.
  • 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_69e2ff3071548190b62d1ac237397197 completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f5ffc74fa481909b4fe24a9337f9eb completed May 2, 2026, 1:44 p.m.
PD Predicate disambiguation batch_69f5f7f99dc08190afcfb3bc4dfbec1d completed May 2, 2026, 1:11 p.m.
Created at: April 18, 2026, 6:26 a.m.