Triple
T6820300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haruko |
E156880
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Haruko |
E156880
|
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: Haruko | Statement: [Haruko, name, Haruko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haruko Context triple: [Haruko, name, Haruko]
-
A.
Haruko
chosen
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.
-
B.
Hanako
Hanako is a Japanese imperial family member best known as Princess Hitachi, the wife of Prince Hitachi, a younger brother of Emperor Emeritus Akihito.
-
C.
Takako
Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
-
D.
Atsuko
Atsuko is a Japanese feminine given name commonly borne by women and princesses in Japan, with meanings that vary depending on the kanji used.
-
E.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
- 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_69c688298a288190af3f285d57f76bbe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d359176c8190a34664ba2fcf7ee2 |
completed | March 27, 2026, 6:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c723e797908190bb0a2d22556b5906 |
completed | March 28, 2026, 12:42 a.m. |
Created at: March 27, 2026, 2:17 p.m.