Triple
T20194905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Take Love Easy |
E493056
|
entity |
| Predicate | hasPrimaryVocalist |
P9645
|
FINISHED |
| Object | Sophie Milman |
—
|
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: Sophie Milman | Statement: [Take Love Easy, hasPrimaryVocalist, Sophie Milman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sophie Milman Context triple: [Take Love Easy, hasPrimaryVocalist, Sophie Milman]
-
A.
Sophie Milman
chosen
Sophie Milman is a Russian-born Canadian jazz vocalist known for her smooth, expressive interpretations of jazz standards and French chansons.
-
B.
Sophie Masson
Sophie Masson is a French-Australian author best known for her prolific work in children's, young adult, and fantasy literature.
-
C.
Sophie Labbé
Sophie Labbé is a renowned French perfumer known for creating numerous successful fragrances for major international luxury brands.
-
D.
Sophie Mas
Sophie Mas is a film producer known for her work on acclaimed independent and international films, including the drama "May December."
-
E.
Sophie Vavasseur
Sophie Vavasseur is an Irish actress best known for her role in the film "Evelyn" and appearances in various horror and drama productions.
- 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_69da6268a034819081cbd9ea5a1c9475 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66ad7ed548190a893110fa2ffb144 |
completed | April 20, 2026, 6:05 p.m. |
Created at: April 11, 2026, 11:37 p.m.