Triple
T17743980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Outsiders |
E442935
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Peter Mattei |
—
|
NE NERFINISHED |
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: Peter Mattei | Statement: [Outsiders, executiveProducer, Peter Mattei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Mattei Context triple: [Outsiders, executiveProducer, Peter Mattei]
-
A.
Peter Mattei
chosen
Peter Mattei is a television writer and producer best known for creating the crime drama series "Outsiders."
-
B.
Max Pilger
Max Pilger is a relatively obscure individual sharing the Pilger surname, with limited publicly available biographical or professional information.
-
C.
Roberto Benabib
Roberto Benabib is a television writer and producer best known for his work on the dark comedy series "Weeds."
-
D.
Giovanni Jona-Lasinio
Giovanni Jona-Lasinio is an Italian theoretical physicist renowned for co-developing the Nambu–Jona-Lasinio model, a foundational framework in quantum field theory and particle physics.
-
E.
Peter Weill
Peter Weill is an Australian-born academic and author best known for his influential work on IT governance and digital business strategy, particularly at the MIT Sloan School of Management.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9ed3a2081909b2ec0d4dd2f4c37 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47acf44f8819089a18193e37d112c |
completed | April 19, 2026, 6:48 a.m. |
Created at: April 10, 2026, 10:09 a.m.