Triple
T7962798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oladapo Daniel Oyebanjo |
E184915
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Fall in Love |
E184918
|
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: Fall in Love | Statement: [Oladapo Daniel Oyebanjo, notableWork, Fall in Love]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fall in Love Context triple: [Oladapo Daniel Oyebanjo, notableWork, Fall in Love]
-
A.
Fall in Love
chosen
"Fall in Love" is a popular Afrobeat love song by Nigerian artist D'banj that became one of his signature hits across Africa.
-
B.
Falling in Love with Love
"Falling in Love with Love" is a popular show tune by composer Richard Rodgers and lyricist Lorenz Hart, introduced in the 1938 musical "The Boys from Syracuse."
-
C.
Falling in Love Again
"Falling in Love Again" is a classic cabaret-style song closely associated with Marlene Dietrich, famously performed in the 1930 film "The Blue Angel."
-
D.
Fell for You
"Fell for You" is a pop-punk song by Green Day from their 2012 album ¡Uno!.
-
E.
Love Is Where It Falls
Love Is Where It Falls is a memoir by British actor and director Simon Callow reflecting on his intense, complex relationship with theatrical agent Peggy Ramsay.
- 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_69ca8293a2388190aace944d7ed9c0c0 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b9df72081908d33925da10192e9 |
completed | March 31, 2026, 3:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe08c36f48190b005c6c92ad813d0 |
completed | March 31, 2026, 2:56 p.m. |
Created at: March 30, 2026, 5:12 p.m.