Triple
T12783120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Muse |
E305555
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Herb Nanas |
E1002312
|
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: Herb Nanas | Statement: [The Muse, producer, Herb Nanas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Herb Nanas Context triple: [The Muse, producer, Herb Nanas]
-
A.
Herb Nanas
chosen
Herb Nanas is a film producer best known for his work on the 1985 comedy-drama "Lost in America."
-
B.
Max Nanasy
Max Nanasy is a software engineer and writer known for contributions to programming tools and documentation, including work related to JSON5.
-
C.
Mr. Nancy
Mr. Nancy is a charismatic, sharp-tongued incarnation of the West African trickster god Anansi who appears as an old man in Neil Gaiman’s American Gods.
-
D.
Reri Grist
Reri Grist is an acclaimed American coloratura soprano renowned for her performances in opera and concert stages worldwide, particularly in works by Mozart and Strauss.
-
E.
Arnie Grape
Arnie Grape is a mentally disabled teenage character from the film "What's Eating Gilbert Grape," known for his vulnerable, unpredictable behavior and deep bond with his brother Gilbert.
- 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_69d7bdf2b43c819098ae5aa68e61ea58 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e5b52048190b279b7ad066efe9f |
completed | April 10, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69b925b3c81909f5e604c0f457645 |
completed | May 3, 2026, 12:49 a.m. |
Created at: April 9, 2026, 5:29 p.m.