Triple
T952333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lombok International Airport |
E20548
|
entity |
| Predicate | locatedOn |
P40
|
FINISHED |
| Object | Lombok |
E20548
|
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: Lombok | Statement: [Lombok International Airport, locatedOn, Lombok]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lombok Context triple: [Lombok International Airport, locatedOn, Lombok]
-
A.
Lombok
chosen
Lombok is an Indonesian island east of Bali, known for its volcanic Mount Rinjani, beaches, and Sasak culture.
-
B.
Yanam
Yanam is a coastal town and district enclave of the Union Territory of Puducherry in India, historically influenced by French colonial rule and culturally linked to the Telugu-speaking region of Andhra Pradesh.
-
C.
Lapa
Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
-
D.
Madura
Madura is an island off the northeastern coast of Java in Indonesia, known for its distinct Madurese culture and traditional bull races.
-
E.
Shiga
Shiga is a landlocked prefecture in central Japan known for encompassing Lake Biwa, the country’s largest freshwater lake, and for its historical sites and natural scenery.
- 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_69a493b0f2fc81908cd227480a5356a1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3d757d08190a475cf47febd05ae |
completed | March 1, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac2a0f4e088190b2fcd01e9475ff5d |
completed | March 7, 2026, 1:37 p.m. |
Created at: March 1, 2026, 7:40 p.m.