Triple

T14905176
Position Surface form Disambiguated ID Type / Status
Subject NYRR 9+1 program E360110 entity
Predicate entryAllocationMethod P77513 FINISHED
Object non-lottery 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: non-lottery | Statement: [NYRR 9+1 program, entryAllocationMethod, non-lottery]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: entryAllocationMethod
Context triple: [NYRR 9+1 program, entryAllocationMethod, non-lottery]
  • A. allocationType chosen
    Indicates the specific manner or category by which resources, responsibilities, or items are assigned or distributed among entities.
  • B. allocatesAccordingTo
    Indicates that one entity distributes or assigns resources, tasks, or responsibilities to others based on a specified rule, criterion, or plan.
  • C. allocation
    Indicates the distribution or assignment of resources, responsibilities, or items among entities according to some rule or plan.
  • D. supportsAllocationMethods
    Indicates that one entity enables, is compatible with, or provides mechanisms for using specific allocation methods associated with another entity.
  • E. allocates
    Indicates the act of assigning or distributing resources, responsibilities, or portions of something to specific entities or purposes.
  • 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_69d827980cbc8190a0c569ae3940a1d9 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69ded60cd5588190b1efecc2b220da69 completed April 15, 2026, 12:04 a.m.
PD Predicate disambiguation batch_69de9a4a14a88190951bb8f4c60bd37b completed April 14, 2026, 7:49 p.m.
Created at: April 10, 2026, 2:12 a.m.