Triple
T20118677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Let It Be Me |
E490536
|
entity |
| Predicate | basedOnWork |
P7125
|
FINISHED |
| Object | Je t'appartiens |
—
|
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: Je t'appartiens | Statement: [Let It Be Me, basedOnWork, Je t'appartiens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Je t'appartiens Context triple: [Let It Be Me, basedOnWork, Je t'appartiens]
-
A.
Je t’appartiens
chosen
"Je t’appartiens" is a classic French chanson of enduring popularity, best known internationally through its English adaptation "Let It Be Me."
-
B.
Que je t’aime
"Que je t’aime" is a famous 1969 French rock and chanson love song by Johnny Hallyday, known for its passionate lyrics and powerful vocal performance.
-
C.
Pour que tu m’aimes encore
"Pour que tu m’aimes encore" is one of Céline Dion’s most iconic French-language ballads, renowned for its emotional lyrics and powerful vocal performance.
-
D.
Je te veux
"Je te veux" is a popular early 20th-century French waltz-song composed by Erik Satie, known for its sensual melody and cabaret origins.
-
E.
Je t’aime
"Je t’aime" is a powerful French pop ballad by Belgian-Canadian singer Lara Fabian, renowned for its emotional intensity and vocal performance.
- 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_69e6673c32bc8190a52875961fbcc5e2 |
completed | April 20, 2026, 5:49 p.m. |
Created at: April 11, 2026, 11:30 p.m.