Triple
T18356832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Help! (film soundtrack) |
E439817
|
entity |
| Predicate | includesSong |
P7178
|
FINISHED |
| Object | You Like Me Too Much |
E1320079
|
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: You Like Me Too Much | Statement: [Help! (film soundtrack), includesSong, You Like Me Too Much]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: You Like Me Too Much Context triple: [Help! (film soundtrack), includesSong, You Like Me Too Much]
-
A.
You Like Me Too Much
chosen
"You Like Me Too Much" is a mid-1960s Beatles song written by George Harrison that appears on their album Help!.
-
B.
I Love You Too Much
"I Love You Too Much" is a song by Stevie Wonder featured on his 1985 album *In Square Circle*.
-
C.
Love You Too Much
"Love You Too Much" is a song by the American rock band Painted.
-
D.
Love Too Much
Love Too Much is a song by British singer-songwriter Keane from their 2019 album "Cause and Effect."
-
E.
Too Much
"Too Much" is a reflective, emotionally charged song by Canadian rapper Drake that appears on his 2013 album *Nothing Was the Same*.
- 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_69d8b918221c8190a9f7b563d64ac677 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e516d776cc8190937d7e1d42a36a3b |
completed | April 19, 2026, 5:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03d772016c8190a24afa02be5ddea3 |
completed | May 13, 2026, 1:44 a.m. |
Created at: April 10, 2026, 10:37 a.m.