Triple
T1360529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seychelles |
E29088
|
entity |
| Predicate | hasIsland |
P970
|
FINISHED |
| Object | Mahé |
E155732
|
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: Mahé | Statement: [Seychelles, hasIsland, Mahé]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mahé Context triple: [Seychelles, hasIsland, Mahé]
-
A.
Mahé
chosen
Mahé is the largest and most populous island of Seychelles, home to the nation’s capital, Victoria, and its main economic and cultural center.
-
B.
Nosy Be
Nosy Be is a popular resort island off the northwest coast of Madagascar, known for its beaches, marine life, and volcanic lakes.
-
C.
Port Louis
Port Louis is the capital and largest city of Mauritius, serving as its main economic, political, and cultural center as well as a key regional port in the Indian Ocean.
-
D.
Agatti
Agatti is a small coral island in India's Lakshadweep archipelago, known for its turquoise lagoon, white-sand beaches, and the main airport connecting the islands to the mainland.
-
E.
Lihou
Lihou is a small tidal island off the west coast of Guernsey in the Channel Islands, known for its rich wildlife, historic priory ruins, and causeway access at low tide.
- 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_69a498d77abc8190913bf57e5f51d2c4 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c2b156b081909c99ada70a969fc0 |
completed | March 1, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acd47d38388190856b4ae9de1e69d7 |
completed | March 8, 2026, 1:44 a.m. |
Created at: March 1, 2026, 7:56 p.m.