Triple

T2873261
Position Surface form Disambiguated ID Type / Status
Subject Arakan E56814 entity
Predicate hasPort P35 FINISHED
Object Sittwe E218985 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: Sittwe | Statement: [Arakan, hasPort, Sittwe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sittwe
Context triple: [Arakan, hasPort, Sittwe]
  • A. Sittwe chosen
    Sittwe is a coastal city in western Myanmar that has been a focal point of political unrest and ethnic tensions, including major protests during the Saffron Revolution.
  • B. Lashio
    Lashio is a key town in northern Myanmar that historically served as an important transport and trade hub, particularly during World War II as the inland gateway to the Burma Road.
  • C. Amarapura
    Amarapura is a former royal city in Myanmar renowned for its role as an early Burmese capital and for landmarks such as the U Bein Bridge.
  • D. Kawthaung
    Kawthaung is a coastal town in southern Myanmar that serves as a key gateway for cross-border trade and travel with Thailand.
  • E. Meiktila
    Meiktila is a city in central Myanmar that served as a key strategic location during World War II, particularly in the Burma Campaign.
  • 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_69ab4a4ced288190ab6d3e062d10f7f6 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abdfe59ef88190b8bdfdd03e8965f3 completed March 7, 2026, 8:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69b0fc5553e08190b9a49f23bb29fcc4 completed March 11, 2026, 5:23 a.m.
Created at: March 6, 2026, 10:03 p.m.