Triple
T10837066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaori Mai Hart |
E255787
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Kaori
Kaori is a feminine given name of Japanese origin commonly associated with meanings related to fragrance or aroma.
|
E891648
|
NE FINISHED |
How this triple was built (4 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: Kaori | Statement: [Kaori Mai Hart, givenName, Kaori]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kaori Context triple: [Kaori Mai Hart, givenName, Kaori]
-
A.
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.
-
B.
Sanae
Sanae is a Japanese feminine given name borne by various notable figures in politics, entertainment, and other fields.
-
C.
Kiyoko
Kiyoko is a Japanese feminine given name that can be written with various kanji combinations, often carrying meanings related to purity or respect.
-
D.
Takako
Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
-
E.
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.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kaori Triple: [Kaori Mai Hart, givenName, Kaori]
Generated description
Kaori is a feminine given name of Japanese origin commonly associated with meanings related to fragrance or aroma.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kaori Target entity description: Kaori is a feminine given name of Japanese origin commonly associated with meanings related to fragrance or aroma.
-
A.
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.
-
B.
Sanae
Sanae is a Japanese feminine given name borne by various notable figures in politics, entertainment, and other fields.
-
C.
Kiyoko
Kiyoko is a Japanese feminine given name that can be written with various kanji combinations, often carrying meanings related to purity or respect.
-
D.
Takako
Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
-
E.
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.
- F. None of above. chosen
Provenance (5 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d746ff70148190b844ab92d796af6c |
completed | April 9, 2026, 6:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e154a582188190af96ae0d5cc08dc4 |
completed | April 16, 2026, 9:29 p.m. |
| NEDg | Description generation | batch_69e1739ed9b88190949125759f42efd2 |
completed | April 16, 2026, 11:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e175ecef0c8190b08052d751f1a608 |
completed | April 16, 2026, 11:51 p.m. |
Created at: April 8, 2026, 9:19 p.m.