Triple
T4718196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greek islands |
E104699
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Mykonos |
E71781
|
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: Mykonos | Statement: [Greek islands, hasPart, Mykonos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mykonos Context triple: [Greek islands, hasPart, Mykonos]
-
A.
Mykonos
chosen
Mykonos is a popular Greek island in the Cyclades known for its whitewashed architecture, vibrant nightlife, and picturesque beaches.
-
B.
Paros
Paros is a popular Greek island in the central Aegean known for its traditional Cycladic villages, beaches, and marble quarries.
-
C.
Skiathos
Skiathos is a small Greek island in the northwest Aegean Sea, renowned for its sandy beaches, pine forests, and vibrant tourist resorts.
-
D.
Skopelos
Skopelos is a Greek island in the western Aegean Sea, known for its lush pine forests, traditional whitewashed villages, and scenic beaches.
-
E.
Karpathos
Karpathos is a Greek island in the southeastern Aegean Sea known for its rugged mountains, traditional villages, and clear, windswept beaches popular with windsurfers.
- 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_69bd43ec4a348190bc41afae43375e71 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd64261af08190b0d5d86b0e7bacc0 |
completed | March 20, 2026, 3:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf8bbef34c8190ae4b22a94e1cf91a |
completed | March 22, 2026, 6:27 a.m. |
Created at: March 20, 2026, 1:18 p.m.