Triple
T19796858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cy Feuer |
E475565
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Little Me |
—
|
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: Little Me | Statement: [Cy Feuer, notableWork, Little Me]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Little Me Context triple: [Cy Feuer, notableWork, Little Me]
-
A.
Little Me
chosen
"Little Me" is a comedic stage musical, with a book by Neil Simon and music by Cy Coleman, in which Martin Short notably starred in a celebrated revival.
-
B.
Little My
Little My is a small, fiercely independent and mischievous girl from Tove Jansson’s Moomin series, known for her sharp tongue, fearlessness, and distinctive topknot hairstyle.
-
C.
Little Man
"Little Man" is a stand-up comedy special by American comedian Gary Owen, showcasing his energetic storytelling and observational humor.
-
D.
Little Man
Little Man is a prominent subsidiary summit of Skiddaw in England’s Lake District, popular with hikers for its fine views and distinctive profile.
-
E.
Little Man
Little Man is a 2006 American comedy film starring Marlon Wayans as a diminutive criminal who poses as a baby to retrieve a stolen diamond.
- 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e653c723548190ac9bfaecaf8afb13 |
completed | April 20, 2026, 4:26 p.m. |
Created at: April 10, 2026, 1:49 p.m.