Triple

T9961578
Position Surface form Disambiguated ID Type / Status
Subject Alvin Kamara E195581 entity
Predicate conference P900 FINISHED
Object NFC E1377 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: NFC | Statement: [Alvin Kamara, conference, NFC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: NFC
Context triple: [Alvin Kamara, conference, NFC]
  • A. NFC chosen
    The NFC (National Football Conference) is one of the two conferences in the National Football League, comprising 16 teams that compete for a spot in the Super Bowl.
  • B. NFC
    NFC (Near Field Communication) is a short-range wireless communication technology commonly used for contactless payments, data exchange, and device pairing between nearby electronic devices.
  • C. NFC
    The National Finance Commission (NFC) is a constitutional body in Pakistan responsible for periodically determining the distribution of financial resources between the federal government and the provinces.
  • D. NFC Forum
    The NFC Forum is an industry consortium that develops and promotes standards and certification programs for Near Field Communication (NFC) technology to ensure interoperability and widespread adoption.
  • E. RFID
    RFID (Radio-Frequency Identification) is a wireless technology that uses electromagnetic fields to automatically identify and track tagged objects, commonly employed in areas like inventory management, access control, and contactless payments.
  • 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_69ca82ebd1288190912f9e4482d1fa35 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb6d37f0c8190946b958c399f3250 completed April 2, 2026, 12:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69d23d904bbc8190ac0b28600ed2e709 completed April 5, 2026, 10:46 a.m.
Created at: March 30, 2026, 8:47 p.m.