Triple

T13404649
Position Surface form Disambiguated ID Type / Status
Subject École française d’Athènes E319920 entity
Predicate hasResearchStationAt P2881 FINISHED
Object Argos E70939 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: Argos | Statement: [École française d’Athènes, hasResearchStationAt, Argos]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Argos
Context triple: [École française d’Athènes, hasResearchStationAt, Argos]
  • A. Argos
    Argos is a major UK-based catalogue and online retailer known for offering a wide range of household goods, electronics, toys, and more through both physical stores and digital channels.
  • B. Argos
    Argos is the common nickname for the Toronto Argonauts, a professional Canadian Football League team based in Toronto.
  • C. Argos chosen
    Argos is one of the oldest continuously inhabited cities in Greece, located in the Peloponnese and historically significant as a major center of ancient Greek civilization.
  • D. Argus
    Argus is a many-eyed giant from Greek mythology best known for his role as a vigilant guardian.
  • E. Argus
    Argus is an early distributed programming language known for pioneering concepts in fault-tolerant, distributed systems and influencing modern object-oriented and concurrent programming.
  • 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_69d806b943cc8190b6af624d385d7e12 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbae4ae47081909b68a9aaa62fd4c7 completed April 12, 2026, 2:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7307904608190ad647f741c08dc42 completed May 3, 2026, 11:24 a.m.
Created at: April 9, 2026, 9:34 p.m.