Triple
T17776527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hana Highway |
E443783
|
entity |
| Predicate | terminusB |
P388
|
FINISHED |
| Object | Hana |
E100137
|
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: Hana | Statement: [Hana Highway, terminusB, Hana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hana Context triple: [Hana Highway, terminusB, Hana]
-
A.
Hana
Hana is a person known primarily as the romantic partner of Kip.
-
B.
Hana
chosen
Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
-
C.
Hana
Hana is a common female given name of Hebrew origin, often associated with meanings like "grace" or "favor."
-
D.
Hana
Hana is a Japanese restaurant located within Tokyo Disney Resort’s Disney Ambassador Hotel, offering themed dining to hotel guests and park visitors.
-
E.
Hana
Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
- 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_69d8b9ef17708190bdf7e2adbf14ddc2 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4871d43a481908aacde69bd8091b0 |
completed | April 19, 2026, 7:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03211d93e081909aaa6287be791407 |
completed | May 12, 2026, 12:46 p.m. |
Created at: April 10, 2026, 10:12 a.m.