Triple

T14271663
Position Surface form Disambiguated ID Type / Status
Subject bombardment of Martaban E353800 entity
Predicate location P40 FINISHED
Object Martaban E276028 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: Martaban | Statement: [bombardment of Martaban, location, Martaban]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Martaban
Context triple: [bombardment of Martaban, location, Martaban]
  • A. Martaban chosen
    Martaban is a historic port city in southern Myanmar that once served as the capital of the Mon kingdom and a key hub in regional maritime trade.
  • B. Shabara
    Shabara was an early Indian philosopher and commentator best known for his influential exegesis on the Purva Mimamsa school of Hindu philosophy.
  • C. Baddo
    Baddo is the popular stage name of Nigerian rapper and singer Olamide, known for his influential role in contemporary Afrobeats and street-hop music.
  • D. Baddo
    Baddo was a Visigothic queen consort, known as the wife of King Reccared I of the Visigoths in late 6th-century Hispania.
  • E. Kalabar
    Kalabar is the primary villain and dark warlock in Disney's "Halloweentown," who seeks to conquer both the magical realm and the human world.
  • 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_69d8278d25148190abf1a8c8f5f533ad completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de65811d7c8190b075909a6570d415 completed April 14, 2026, 4:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd326a5aec8190b139a0c49fd43705 completed May 8, 2026, 12:46 a.m.
Created at: April 10, 2026, 1:10 a.m.