Triple
T10611995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mottama |
E276028
|
entity |
| Predicate | currentNameOf |
P1213
|
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: [Mottama, currentNameOf, Martaban]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martaban Context triple: [Mottama, currentNameOf, 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6df5a1450819082ad445712fb7868 |
completed | April 8, 2026, 11:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d95ec61b388190adee9b3577567709 |
completed | April 10, 2026, 8:34 p.m. |
Created at: April 8, 2026, 7:33 p.m.