Triple
T2506512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joanne Schieble |
E52595
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object | Mona Simpson |
E62082
|
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: Mona Simpson | Statement: [Joanne Schieble, child, Mona Simpson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mona Simpson Context triple: [Joanne Schieble, child, Mona Simpson]
-
A.
Mona Simpson
chosen
Mona Simpson is an American novelist and professor known for works such as "Anywhere but Here" and for being the biological sister of Apple co-founder Steve Jobs.
-
B.
Molly Smith
Molly Smith is a daughter of FedEx founder and CEO Frederick W. Smith.
-
C.
Molly Smith
Molly Smith is an American film producer known for her work on acclaimed movies such as the crime thriller "Sicario."
-
D.
Sandra Hunt
Sandra Hunt is best known as the wife of legendary Los Angeles Dodgers broadcaster Vin Scully.
-
E.
Rebecca Giblin
Rebecca Giblin is an Australian legal scholar and advocate specializing in copyright, technology, and creators’ rights, known for her work on how digital platforms affect cultural industries.
- 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_69ab4958e76481908a235377dd921c9e |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1cfeb408190ba8107296310dbfc |
completed | March 7, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af1fa7dbb88190815087416b207b54 |
completed | March 9, 2026, 7:29 p.m. |
Created at: March 6, 2026, 9:46 p.m.