Triple
T942011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rama Navami |
E20325
|
entity |
| Predicate | observedIn |
P40
|
FINISHED |
| Object | Mauritius |
E28590
|
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: Mauritius | Statement: [Rama Navami, observedIn, Mauritius]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mauritius Context triple: [Rama Navami, observedIn, Mauritius]
-
A.
Mauritius
chosen
Mauritius is an island nation in the Indian Ocean known for its multicultural society, stable democracy, and tourism-driven economy.
-
B.
Seychelles
Seychelles is an Indian Ocean island nation off the coast of East Africa, known for its tropical beaches, coral reefs, and unique biodiversity.
-
C.
Madagascar
Madagascar is a large island nation in the Indian Ocean renowned for its unique biodiversity and high rate of endemic species.
-
D.
Comoros
Comoros is an island nation in the Indian Ocean off the eastern coast of Africa, known for its diverse cultural heritage and history as a former French colony.
-
E.
Maldives
The Maldives is a tropical island nation in the Indian Ocean renowned for its white-sand beaches, coral reefs, and luxury overwater resorts.
- 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_69a493b0270c81909e6c9ce310f6aa55 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3a1a4888190997adf56eb761431 |
completed | March 1, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acc5ff51c48190ab9b096d9d885d8f |
completed | March 8, 2026, 12:42 a.m. |
Created at: March 1, 2026, 7:40 p.m.