Triple
T16102580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laura Kōnia |
E390657
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Laura Konia |
E390657
|
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: Laura Konia | Statement: [Laura Kōnia, alsoKnownAs, Laura Konia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laura Konia Context triple: [Laura Kōnia, alsoKnownAs, Laura Konia]
-
A.
Laura Kōnia
chosen
Laura Kōnia was a high-ranking Hawaiian aliʻi (noblewoman) of the 19th century and a member of the royal lineage closely connected to the Kingdom of Hawaiʻi’s ruling families.
-
B.
Kimberly Krysiuk
Kimberly Krysiuk is a writer known for her work on the series "Baby Mama."
-
C.
Anna Konkle
Anna Konkle is an American actress, writer, and producer best known for co-creating and starring in the coming-of-age comedy series "PEN15."
-
D.
Jennifer Kluska
Jennifer Kluska is an American film director and storyboard artist best known for her work in animated features, including the Hotel Transylvania franchise.
-
E.
Karen Zariakas
Karen Zariakas is the central female protagonist in the 2019 neo-noir thriller film "Serenity," whose mysterious reappearance drives the plot’s psychological and moral conflicts.
- 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_69d87f1a8dd881909f1de6ef78849874 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e1ff6976ec8190b499e99b196b0285 |
completed | April 17, 2026, 9:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000ec4d9808190a3d1bfc8f3d73168 |
completed | May 10, 2026, 4:51 a.m. |
Created at: April 10, 2026, 5 a.m.