Triple
T22586024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morton |
E564794
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Kate Morton |
E87215
|
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: Kate Morton | Statement: [Morton, hasNotableBearer, Kate Morton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Morton Context triple: [Morton, hasNotableBearer, Kate Morton]
-
A.
Kate Morton
chosen
Kate Morton is an Australian bestselling novelist known for her atmospheric historical mysteries such as "The Forgotten Garden" and "The House at Riverton."
-
B.
Lisa Riley
Lisa Riley is a British actress and television presenter best known for her roles in popular UK dramas and for hosting the ITV game show "You've Been Framed!".
-
C.
Linda Howard
Linda Howard is a fictional protagonist featured in the film "Lost in America."
-
D.
Kate Ellis
Kate Ellis is the responsible, straight-laced older sister portrayed by Tina Fey in the 2015 comedy film "Sisters," whose attempts to manage a final blowout party with her sibling drive much of the movie’s humor and heart.
-
E.
Kate Ellis
Kate Ellis is a British crime novelist known for her mystery series that blend contemporary detective work with historical elements.
- 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_69e245836014819091b91ed3074742a3 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1615d1fe081908079f777cdab12a2 |
completed | April 29, 2026, 1:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b3d584fb88190b64be86c696a25eb |
completed | May 18, 2026, 4:24 p.m. |
Created at: April 17, 2026, 2:46 p.m.