Triple
T16220492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Takako |
E393709
|
entity |
| Predicate | canBeRomanizedAs |
P2508
|
FINISHED |
| Object | Takako |
—
|
NE NERFINISHED |
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: Takako | Statement: [Takako, canBeRomanizedAs, Takako]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Takako Context triple: [Takako, canBeRomanizedAs, Takako]
-
A.
Takako
chosen
Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
-
B.
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.
-
C.
Keiko
Keiko was a famous captive orca best known for starring in the film "Free Willy" and later becoming the focus of a high-profile rehabilitation and release effort.
-
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.
Chikako
Chikako is a Japanese feminine given name that can be written with various kanji characters and is borne by several notable women in Japan.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d87f204df88190a8f88923decf9835 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e227fabf708190a624c1ed8ce48b0a |
completed | April 17, 2026, 12:30 p.m. |
Created at: April 10, 2026, 5:03 a.m.