Triple
T371795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southeast Asia |
E8284
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Yangon |
E42684
|
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: Yangon | Statement: [Southeast Asia, hasMajorCity, Yangon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yangon Context triple: [Southeast Asia, hasMajorCity, Yangon]
-
A.
Yangon
chosen
Yangon is Myanmar’s largest city and former capital, known as a major commercial hub featuring a mix of colonial architecture and prominent Buddhist landmarks like the Shwedagon Pagoda.
-
B.
Chiang Mai
Chiang Mai is a historic city in northern Thailand known for its ancient temples, vibrant night markets, and surrounding mountainous landscapes.
-
C.
Bangkok
Bangkok is the vibrant capital and largest city of Thailand, known for its bustling street life, ornate temples, and role as a major economic and cultural hub in Southeast Asia.
-
D.
Ulaanbaatar
Ulaanbaatar is the capital and largest city of Mongolia, serving as its political, economic, and cultural center.
-
E.
Nara
Nara is an ancient Japanese city renowned for its early role as a national capital, its historic temples, and its culturally significant deer-filled parks.
- 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_69a2e7f2ec648190b42bc7db424f8109 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec00785481908551fc3571fcca47 |
completed | Feb. 28, 2026, 1:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3f0a78c748190ae5e64919f1d6501 |
completed | March 1, 2026, 7:54 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.