Triple

T22446135
Position Surface form Disambiguated ID Type / Status
Subject Gilad Bracha E554866 entity
Predicate employer P7 FINISHED
Object SAP NE NERFINISHED

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: SAP | Statement: [Gilad Bracha, employer, SAP]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SAP
Context triple: [Gilad Bracha, employer, SAP]
  • A. SAP chosen
    SAP is a leading global enterprise software company best known for its ERP solutions that help organizations manage business operations and customer relations.
  • B. SAP
    SAP is the commonly used abbreviation for the Société d’Anthropologie de Paris, a French learned society dedicated to the study of anthropology.
  • C. SAP
    SAP is the station code for Lisbon Santa Apolónia, one of the main railway terminals in Lisbon, Portugal.
  • D. SAP
    SAP is the IATA airport code for Ramón Villeda Morales International Airport, the main air gateway serving San Pedro Sula, Honduras.
  • E. SAP
    SAP (Session Announcement Protocol) is a network protocol used to broadcast multicast session information, typically carrying Session Description Protocol (SDP) data to announce multimedia sessions over IP networks.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e5113208190ab58c6b595f9d1d0 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15b4803908190990280ebd258cb03 completed April 29, 2026, 1:13 a.m.
Created at: April 16, 2026, 8:47 p.m.