Triple
T17360712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sigrid |
E422058
|
entity |
| Predicate | hasNotableSingle |
P3283
|
FINISHED |
| Object | Mirror |
—
|
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: Mirror | Statement: [Sigrid, hasNotableSingle, Mirror]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mirror Context triple: [Sigrid, hasNotableSingle, Mirror]
-
A.
Mirror
"Mirror" is a reflective, introspective hip-hop/R&B song by Lil Wayne featuring Bruno Mars, known for its emotional lyrics and soulful hook.
-
B.
Mirror
chosen
"Mirror" is a popular electropop song by Norwegian singer-songwriter Sigrid, known for its empowering lyrics about self-acceptance and confidence.
-
C.
Mirror
"Mirror" is an early EP by the American dance-punk band The Rapture, showcasing their raw, experimental fusion of post-punk and electronic influences.
-
D.
Mirror
Mirror is a 1975 Russian art film by director Andrei Tarkovsky that blends poetry, memory, and dreamlike imagery in a non-linear meditation on personal and collective history.
-
E.
Mirror Image
"Mirror Image" is an episode of the classic science fiction television series The Twilight Zone, known for its eerie story about a woman who encounters her sinister doppelgänger in a bus station.
- 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_69d889d520008190a26917a95bf1c2ea |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a4cacd881909fd722068b019f25 |
completed | April 19, 2026, 2:13 a.m. |
Created at: April 10, 2026, 5:44 a.m.