Triple
T19796861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cy Feuer |
E475565
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Walking Happy
Walking Happy is a 1966 Broadway musical adaptation of the play "Hobson's Choice," featuring music by Jimmy Van Heusen and lyrics by Sammy Cahn.
|
E1395621
|
NE FINISHED |
How this triple was built (4 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: Walking Happy | Statement: [Cy Feuer, notableWork, Walking Happy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Walking Happy Context triple: [Cy Feuer, notableWork, Walking Happy]
-
A.
Sing Happy
"Sing Happy" is an upbeat show tune from the 1965 Broadway musical *Flora the Red Menace*, famously performed by Liza Minnelli.
-
B.
So Happy
"So Happy" is a rock song by Canadian band Theory of a Deadman, known for its dark, hard-edged sound and themes of toxic relationships.
-
C.
Be Happy
"Be Happy" is a song featured on the album *My Life*, likely contributing an uplifting or optimistic theme to the record.
-
D.
Long Way to Happy
"Long Way to Happy" is a song by Pink from her album "I'm Not Dead," known for its emotionally charged lyrics about trauma and resilience.
-
E.
Getting to Happy
Getting to Happy is a 2010 novel by Terry McMillan that revisits the lives of the four friends from Waiting to Exhale as they navigate middle age, love, loss, and personal renewal.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Walking Happy Triple: [Cy Feuer, notableWork, Walking Happy]
Generated description
Walking Happy is a 1966 Broadway musical adaptation of the play "Hobson's Choice," featuring music by Jimmy Van Heusen and lyrics by Sammy Cahn.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Walking Happy Target entity description: Walking Happy is a 1966 Broadway musical adaptation of the play "Hobson's Choice," featuring music by Jimmy Van Heusen and lyrics by Sammy Cahn.
-
A.
Sing Happy
"Sing Happy" is an upbeat show tune from the 1965 Broadway musical *Flora the Red Menace*, famously performed by Liza Minnelli.
-
B.
So Happy
"So Happy" is a rock song by Canadian band Theory of a Deadman, known for its dark, hard-edged sound and themes of toxic relationships.
-
C.
Be Happy
"Be Happy" is a song featured on the album *My Life*, likely contributing an uplifting or optimistic theme to the record.
-
D.
Long Way to Happy
"Long Way to Happy" is a song by Pink from her album "I'm Not Dead," known for its emotionally charged lyrics about trauma and resilience.
-
E.
Getting to Happy
Getting to Happy is a 2010 novel by Terry McMillan that revisits the lives of the four friends from Waiting to Exhale as they navigate middle age, love, loss, and personal renewal.
- F. None of above. chosen
Provenance (5 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. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07c504a21c819080be22b7219a0016 |
completed | May 16, 2026, 1:14 a.m. |
| NEDg | Description generation | batch_6a07c66178108190a6fc2d7566af44ad |
completed | May 16, 2026, 1:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07c73b731881909c8abaeb541e34f9 |
completed | May 16, 2026, 1:24 a.m. |
Created at: April 10, 2026, 1:49 p.m.