Triple
T3066387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tea with Mussolini |
E62112
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Cher |
E92362
|
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: Cher | Statement: [Tea with Mussolini, starring, Cher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cher Context triple: [Tea with Mussolini, starring, Cher]
-
A.
Cher
Cher is a department in central France, named after the Cher River and known for its historic towns, vineyards, and agricultural landscapes.
-
B.
Cher
chosen
Cher is an American singer, actress, and pop culture icon known for her distinctive contralto voice, decades-spanning career, and hits like "Believe" and "If I Could Turn Back Time."
-
C.
Olivia Newton-John
Olivia Newton-John was a British-Australian singer and actress best known for her role as Sandy in the film musical "Grease" and for hit songs such as "Physical."
-
D.
Bette Midler
Bette Midler is an American singer, actress, and comedian renowned for her powerful vocals, theatrical performances, and acclaimed work in film, television, and on stage.
-
E.
La La Anthony
La La Anthony is an American television personality and actress known for her roles in film and TV as well as her work as a host and producer.
- 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_69ad85793e5c8190a358049bc4a98d8c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada0fd87308190918e7b616f033faa |
completed | March 8, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1ef16cf2881908265dfe8a1e3424d |
completed | March 11, 2026, 10:39 p.m. |
Created at: March 8, 2026, 3:02 p.m.