Triple

T15245746
Position Surface form Disambiguated ID Type / Status
Subject Anzhu Islands E364374 entity
Predicate hasIsland P970 FINISHED
Object Bunge Land E347618 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: Bunge Land | Statement: [Anzhu Islands, hasIsland, Bunge Land]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bunge Land
Context triple: [Anzhu Islands, hasIsland, Bunge Land]
  • A. Bunge Land chosen
    Bunge Land is a low-lying, largely sandy Arctic island or landmass within Russia’s New Siberian Islands archipelago, known for being periodically flooded by the sea.
  • B. Bunge
    Bunge is a surname most notably associated with Nikolai Bunge, a prominent 19th-century Russian economist and statesman.
  • C. Bunge
    Bunge is the unicameral legislative body and main law-making institution of the United Republic of Tanzania.
  • D. Labone
    Labone is a primarily residential and upscale neighborhood in Accra, Ghana, known for its embassies, guesthouses, and proximity to the coastal areas.
  • E. Felda
    Felda is a small river in central Germany that flows through Hesse and Thuringia before joining the Werra.
  • 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_69d85a0dde7481908fc64d1e82d5d20d completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e007f306f08190be448b215d6c9b6c completed April 15, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fedd461cf08190a506aac2f0cec83a completed May 9, 2026, 7:07 a.m.
Created at: April 10, 2026, 3:13 a.m.