Triple
T6627794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Two and a Half Men |
E149847
|
entity |
| Predicate | composer |
P1361
|
FINISHED |
| Object | Lee Aronsohn |
E608542
|
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: Lee Aronsohn | Statement: [Two and a Half Men, composer, Lee Aronsohn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lee Aronsohn Context triple: [Two and a Half Men, composer, Lee Aronsohn]
-
A.
Lee Aronsohn
chosen
Lee Aronsohn is an American television writer and producer best known for co-creating the hit sitcom "Two and a Half Men."
-
B.
Ari Leschnikoff
Ari Leschnikoff was a Bulgarian-born tenor and entertainer best known as a member of the renowned German vocal group the Comedian Harmonists in the early 20th century.
-
C.
Jeremy Ashkenas
Jeremy Ashkenas is an American programmer and open-source developer best known for creating the CoffeeScript language and contributing to projects like Backbone.js and Underscore.js.
-
D.
Ali Weinberg
Ali Weinberg is an American journalist and television news producer known for her work covering politics for major U.S. news networks.
-
E.
Uriel Frisch
Uriel Frisch is a French physicist and mathematician renowned for his contributions to fluid dynamics and turbulence theory.
- 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_69c687ee50048190aa151765bef16193 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6afa2e4a48190ba3c70013bab14f2 |
completed | March 27, 2026, 4:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6f78f667c81908c2de74009c8e073 |
completed | March 27, 2026, 9:33 p.m. |
Created at: March 27, 2026, 1:59 p.m.