Triple

T17560118
Position Surface form Disambiguated ID Type / Status
Subject UFS E427676 entity
Predicate alternativeName P39 FINISHED
Object FFS 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: FFS | Statement: [UFS, alternativeName, FFS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: FFS
Context triple: [UFS, alternativeName, FFS]
  • A. FFS
    FFS is the commonly used abbreviation for the Swiss Federal Railways, the national railway company of Switzerland.
  • B. FFS
    FFS is a high-performance file system originally developed for BSD Unix that introduced improved disk layout and efficiency over earlier Unix file systems.
  • C. FFS
    FFS is the station code for Frankfurt (Main) Süd, a major railway station in Frankfurt, Germany.
  • D. FFS
    FFS is the state agency responsible for managing and protecting Florida’s forest resources, including wildfire prevention, suppression, and sustainable forestry.
  • E. FFS
    FFS is the abbreviation for the Football Federation Samoa, the governing body responsible for overseeing football activities in Samoa.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide. chosen

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_69d889e0385081908a04b66f4dd4bd0d completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e4562573e48190a19f30fe915a5455 completed April 19, 2026, 4:12 a.m.
Created at: April 10, 2026, 5:50 a.m.