Triple

T1040040
Position Surface form Disambiguated ID Type / Status
Subject Shahnameh E22449 entity
Predicate metre P6963 FINISHED
Object motaqareb metre
The motaqareb metre is a classical Persian poetic meter famously used throughout Ferdowsi’s epic Shahnameh and many other traditional Persian works.
 E120196 NE FINISHED

How this triple was built (4 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: motaqareb metre | Statement: [Shahnameh, metre, motaqareb metre]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: motaqareb metre
Context triple: [Shahnameh, metre, motaqareb metre]
  • A. მტკვარი
    მტკვარი არის სამხრეთ კავკასიის ერთ-ერთი უმთავრესი მდინარე, რომელიც საქართველოს დედაქალაქ თბილისს კვეთს და რეგიონში მნიშვნელოვანი ისტორიული, ეკონომიკური და კულტურული მნიშვნელობა აქვს.
  • B. Motal
    Motal is a small town in present-day Belarus, historically part of the Russian Empire, known as the birthplace of Israel’s first president, Chaim Weizmann.
  • C. Mie
    Mie is a prefecture in central Japan known for its coastal landscapes, historic Ise Grand Shrine, and traditional pearl cultivation.
  • D. Mille
    Mille is a French surname most notably borne by individuals such as Stéphane Mille.
  • E. Martz
    Martz is a surname most notably associated with Mike Martz, an American football coach known for his innovative offensive strategies in the NFL.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: motaqareb metre
Triple: [Shahnameh, metre, motaqareb metre]
Generated description
The motaqareb metre is a classical Persian poetic meter famously used throughout Ferdowsi’s epic Shahnameh and many other traditional Persian works.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: motaqareb metre
Target entity description: The motaqareb metre is a classical Persian poetic meter famously used throughout Ferdowsi’s epic Shahnameh and many other traditional Persian works.
  • A. მტკვარი
    მტკვარი არის სამხრეთ კავკასიის ერთ-ერთი უმთავრესი მდინარე, რომელიც საქართველოს დედაქალაქ თბილისს კვეთს და რეგიონში მნიშვნელოვანი ისტორიული, ეკონომიკური და კულტურული მნიშვნელობა აქვს.
  • B. Motal
    Motal is a small town in present-day Belarus, historically part of the Russian Empire, known as the birthplace of Israel’s first president, Chaim Weizmann.
  • C. Mie
    Mie is a prefecture in central Japan known for its coastal landscapes, historic Ise Grand Shrine, and traditional pearl cultivation.
  • D. Mille
    Mille is a French surname most notably borne by individuals such as Stéphane Mille.
  • E. Martz
    Martz is a surname most notably associated with Mike Martz, an American football coach known for his innovative offensive strategies in the NFL.
  • F. None of above. chosen

Provenance (5 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_69a493d91478819094cc01fb65564bc1 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b82e4d2c81909ca1264852baf04d completed March 1, 2026, 10:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac3bc58d8c8190b9dc7a4bc986abcb completed March 7, 2026, 2:52 p.m.
NEDg Description generation batch_69ac3cf534008190a034d71c90f35efd completed March 7, 2026, 2:57 p.m.
NED2 Entity disambiguation (via description) batch_69ac3d61a0c48190b619b3049df33512 completed March 7, 2026, 2:59 p.m.
Created at: March 1, 2026, 7:41 p.m.