Triple
T3041434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kapalua Airport |
E83138
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Kapalua |
E327736
|
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: Kapalua | Statement: [Kapalua Airport, serves, Kapalua]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kapalua Context triple: [Kapalua Airport, serves, Kapalua]
-
A.
Kapalua
chosen
Kapalua is a resort community on the northwest coast of Maui, Hawaii, known for its luxury accommodations, golf courses, and scenic beaches.
-
B.
Nānākuli
Nānākuli is a coastal community on the leeward side of Oʻahu in Hawaii, known for its strong Native Hawaiian presence and scenic beaches.
-
C.
Kihei
Kihei is a coastal town on the southwest shore of Maui, Hawaii, known for its sunny weather, beaches, and resort and residential communities.
-
D.
Kailua-Kona
Kailua-Kona is a coastal town on the west side of Hawaii's Big Island known for its sunny beaches, historic sites, coffee farms, and role as a major tourist and commercial center.
-
E.
Līhuʻe
Līhuʻe is the principal town and administrative center of Kauai County on the Hawaiian island of Kauai.
- 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_69ad8b2298908190a7cb4e9bdbf064d0 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9b5b92088190971bed04e65c5917 |
completed | March 8, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b261e211c0819094ef28112bcad250 |
completed | March 12, 2026, 6:49 a.m. |
Created at: March 8, 2026, 3:01 p.m.