Triple
T13052839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cabot |
E327489
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Meg Cabot |
E574319
|
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: Meg Cabot | Statement: [Cabot, hasNotableBearer, Meg Cabot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meg Cabot Context triple: [Cabot, hasNotableBearer, Meg Cabot]
-
A.
Meg Cabot
chosen
Meg Cabot is an American author best known for her popular young adult novels, particularly the bestselling "The Princess Diaries" series.
-
B.
Maureen Johnson
Maureen Johnson is a flamboyant, performance-artist character in the musical "Rent," known for her dramatic personality and complex romantic relationships.
-
C.
Sarah Shephard
Sarah Shephard is a fictional character from the television series "Lost," known as Jack Shephard’s ex-wife.
-
D.
Gabriella Wilde
Gabriella Wilde is an English actress and model known for roles in films such as "The Three Musketeers," "Carrie," and "Endless Love."
-
E.
Meg Rosoff
Meg Rosoff is an American-born British author best known for her award-winning young adult novels that often explore dark, complex themes with a distinctive, lyrical style.
- 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d980b98fa081908cfa92116799e874 |
completed | April 10, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbdcc3e881908d9a246558b1c20e |
completed | May 3, 2026, 4:15 a.m. |
Created at: April 9, 2026, 8:58 p.m.