Triple
T3189781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neil Goldman |
E66791
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Muriel Goldman |
E293455
|
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: Muriel Goldman | Statement: [Neil Goldman, associatedWith, Muriel Goldman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Muriel Goldman Context triple: [Neil Goldman, associatedWith, Muriel Goldman]
-
A.
Muriel Goldman
chosen
Muriel Goldman is a minor recurring character in the animated television series "Family Guy," known as the wife of Mort Goldman and mother of Neil Goldman.
-
B.
Margaret Shenberg
Margaret Shenberg was the first wife of influential Hollywood film producer and studio executive Louis B. Mayer.
-
C.
Miriam Mendelsohn
Miriam Mendelsohn is a loyal, upbeat, and supportive best friend of Mei Lee in Pixar's animated film "Turning Red."
-
D.
Maria Nuzberg
Maria Nuzberg was the wife of Vasily Stalin, the son of Soviet leader Joseph Stalin.
-
E.
Ruth Arnon
Ruth Arnon is an Israeli biochemist best known as a co-developer of the multiple sclerosis drug Copaxone and a prominent figure in immunology research.
- 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_69ad8588ba18819086a10951c32ecb80 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada6e67e948190afbd9cc6a3ade415 |
completed | March 8, 2026, 4:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b24b9a9bc88190b7090bda8fe6260c |
completed | March 12, 2026, 5:14 a.m. |
Created at: March 8, 2026, 3:07 p.m.