Triple

T1427098
Position Surface form Disambiguated ID Type / Status
Subject Port of Banana E30357 entity
Predicate nearbyCity P350 FINISHED
Object Muanda E153801 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: Muanda | Statement: [Port of Banana, nearbyCity, Muanda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Muanda
Context triple: [Port of Banana, nearbyCity, Muanda]
  • A. Muanda chosen
    Muanda is a coastal town in the Democratic Republic of the Congo situated near the mouth of the Congo River on the Atlantic Ocean.
  • B. Maand
    Maand is a classical folk music style from Rajasthan, India, known for its expressive melodies and royal courtly associations.
  • C. MON
    MON is the standard abbreviation used for the Montreal Canadiens, the historic National Hockey League team based in Montreal, Quebec.
  • D. MON
    MON is the commonly used abbreviation for Poland’s Ministry of National Defence, the government body responsible for the country’s defense policy and armed forces.
  • E. Madruga
    Madruga is a municipality in western Cuba known for its rural character and location within the historical region surrounding Havana.
  • 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_69a498fb823c8190a67ce4c4837e641a completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c4bfc79481908d370ec839ddbd9f completed March 1, 2026, 10:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad016708bc8190af01b10a0f0d6942 completed March 8, 2026, 4:56 a.m.
Created at: March 1, 2026, 8 p.m.