Triple
T16824517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haruka Satō |
E408982
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Haruka |
E922894
|
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: Haruka | Statement: [Haruka Satō, givenName, Haruka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haruka Context triple: [Haruka Satō, givenName, Haruka]
-
A.
Haruka
chosen
Haruka is a Japanese limited express train service operated by JR West that primarily connects Kansai International Airport with Kyoto and other major destinations in the Kansai region.
-
B.
Tsutako
Tsutako is a Japanese given name, most notably borne by Tsutako Nakasone.
-
C.
Hisako
Hisako is a member of the Japanese imperial family known as Princess Takamado, recognized for her cultural, charitable, and international goodwill activities.
-
D.
Haruko
Haruko, better known as Empress Shōken, was the consort of Emperor Meiji and a prominent Japanese empress noted for her support of modernization and social welfare.
-
E.
Harumi
Harumi is a waterfront district in Tokyo’s Chūō ward known for its high-rise residential towers and role in the Tokyo 2020 Olympic and Paralympic Village.
- 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_69d88394566c8190b3dcbdc72935f7fa |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b310ffec81908087e5aaacc4a7c2 |
completed | April 18, 2026, 4:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b29c170c81908fcc88c31e266ffb |
completed | May 10, 2026, 4:30 p.m. |
Created at: April 10, 2026, 5:23 a.m.