Triple
T5320976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Going to a Town |
E121670
|
entity |
| Predicate | hasCoverVersionBy |
P11142
|
FINISHED |
| Object | Mika |
E66959
|
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: Mika | Statement: [Going to a Town, hasCoverVersionBy, Mika]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mika Context triple: [Going to a Town, hasCoverVersionBy, Mika]
-
A.
Mika
chosen
Mika is a British-Lebanese pop singer-songwriter known for his flamboyant style and hit singles like "Grace Kelly."
-
B.
Mika Miko
Mika Miko was a Los Angeles-based punk band known for its frenetic live shows and raw, lo-fi sound that drew from hardcore and post-punk influences.
-
C.
Martika
Martika is an American pop singer and former child actress best known for her late-1980s hits like "Toy Soldiers."
-
D.
Mya
Mya is a central female character in the romantic comedy film "Think Like a Man," known for following Steve Harvey’s dating advice as she navigates modern relationships.
-
E.
Rema
Rema is a Nigerian singer, songwriter, and rapper known for his influential role in modern Afrobeats and hit songs like "Dumebi" and "Calm Down."
- 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_69bd463d956c819088105c3db802c017 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85761ec48190879210af116ed8b9 |
completed | March 20, 2026, 5:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf18a4dff48190bce18f0106c7a8e7 |
completed | March 21, 2026, 10:16 p.m. |
Created at: March 20, 2026, 1:59 p.m.