Triple

T9925415
Position Surface form Disambiguated ID Type / Status
Subject Arak E187909 entity
Predicate locatedIn P40 FINISHED
Object Markazi Province E779987 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: Markazi Province | Statement: [Arak, locatedIn, Markazi Province]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Markazi Province
Context triple: [Arak, locatedIn, Markazi Province]
  • A. Markazi Province chosen
    Markazi Province is a central region of Iran known for its industrial cities and strategic location connecting major parts of the country.
  • B. Rehamna Province
    Rehamna Province is an administrative division in central Morocco known for its rural communities and agricultural activities within the Marrakesh-Safi region.
  • C. Chahar Province
    Chahar Province was a former province of northern China that historically encompassed parts of what are now Hebei and Inner Mongolia and served as a strategic frontier region.
  • D. Lorestan Province
    Lorestan Province is a mountainous region in western Iran known for its Lur population, rich history, and scenic landscapes of the Zagros Mountains.
  • E. South Khorasan Province
    South Khorasan Province is an eastern Iranian province formed from the historical region of Khorasan, known for its deserts, saffron production, and border with Afghanistan.
  • 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_69ca82b22a688190b52c75bd48429c10 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cdb599e32c8190ac676fa89c131bb6 completed April 2, 2026, 12:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69e4aca16a10819097bb8655c8c1a36d completed April 19, 2026, 10:21 a.m.
Created at: March 30, 2026, 8:43 p.m.