Triple
T14162157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Nori |
E350974
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Sayako |
E1083155
|
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: Sayako | Statement: [Princess Nori, givenName, Sayako]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sayako Context triple: [Princess Nori, givenName, Sayako]
-
A.
Sayako
chosen
Sayako is the only daughter of Japan's former Emperor Akihito and Empress Michiko, who left the imperial family upon her marriage to a commoner.
-
B.
Kiyoko
Kiyoko is a Japanese feminine given name that can be written with various kanji combinations, often carrying meanings related to purity or respect.
-
C.
Kyoko
Kyoko is a mysterious, mostly silent android in the science fiction film "Ex Machina," serving as both assistant and unsettling presence within the reclusive inventor Nathan's isolated research facility.
-
D.
Naoko
Naoko is a central, emotionally fragile character in Haruki Murakami’s story "Norwegian Wood," whose complex relationship with the protagonist explores themes of love, loss, and mental illness.
-
E.
Takako
Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
- 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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de613a4a2081908fd51bf4b4d82b6c |
completed | April 14, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd193b72f48190b80ac30d32ab8349 |
completed | May 7, 2026, 10:59 p.m. |
Created at: April 10, 2026, 12:59 a.m.