Triple
T14902551
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hendrika |
E360042
|
entity |
| Predicate | hasShortForm |
P43
|
FINISHED |
| Object | Rika |
E1085520
|
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: Rika | Statement: [Hendrika, hasShortForm, Rika]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rika Context triple: [Hendrika, hasShortForm, Rika]
-
A.
Rika
chosen
Rika is a feminine given name, often used as a short or diminutive form of longer names such as Henrike, Frederika, or Erika.
-
B.
Takako
Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
-
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.
Risa
Risa is a character in August Wilson's play "Two Trains Running," known as a young, resilient waitress whose personal struggles and aspirations reflect the broader social and emotional tensions of the story.
-
E.
Tokai Rika
Tokai Rika is a Japanese automotive parts manufacturer best known for producing switches, security systems, and other electronic components for major carmakers.
- 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_69d827980cbc8190a0c569ae3940a1d9 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69ded60b24008190bd272c0d61329400 |
completed | April 15, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe72b4e4f88190af7e859d93dbbd28 |
completed | May 8, 2026, 11:33 p.m. |
Created at: April 10, 2026, 2:11 a.m.