Triple
T20105895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faron Young |
E490176
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Hello Walls |
—
|
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: Hello Walls | Statement: [Faron Young, notableWork, Hello Walls]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hello Walls Context triple: [Faron Young, notableWork, Hello Walls]
-
A.
Hello Walls
chosen
"Hello Walls" is a classic country song written by Willie Nelson that became a major hit in 1961 and helped establish him as a prominent songwriter.
-
B.
How Many Walls
"How Many Walls" is a song by the American metalcore band Wolves at the Gate.
-
C.
Walls
Walls is a well-known ice cream brand in Unilever’s global frozen desserts portfolio, recognized for its wide range of frozen treats sold under various local names worldwide.
-
D.
Walls
"Walls" is a 2016 studio album by American rock band Kings of Leon that marked a stylistic shift with more polished production and introspective songwriting.
-
E.
Walls
Walls is a civil parish in Orkney, Scotland, encompassing rural communities and coastal landscapes on the island of Hoy.
- 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_69da62636cc08190982cc71733a17b8d |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e666dbad30819082454f360f358131 |
completed | April 20, 2026, 5:48 p.m. |
Created at: April 11, 2026, 11:28 p.m.