Triple
T15595783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martika |
E374887
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Martika |
E374887
|
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: Martika | Statement: [Martika, name, Martika]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martika Context triple: [Martika, name, Martika]
-
A.
Martika
chosen
Martika is an American pop singer and former child actress best known for her late-1980s hits like "Toy Soldiers."
-
B.
Lili Damita
Lili Damita was a French-American actress and former dancer known for her roles in early sound films and for her high-profile marriage to Hollywood star Errol Flynn.
-
C.
Fairuza Balk
Fairuza Balk is an American actress known for her intense, offbeat roles in films such as "The Craft," "American History X," and "Almost Famous."
-
D.
Lupe Izzo
Lupe Izzo is the wife of longtime Michigan State University men's basketball coach Tom Izzo and is known for her involvement in community and charitable activities connected to the program.
-
E.
Inna
Inna is a Romanian dance-pop singer known for international hits like "Hot" and "Sun Is Up."
- 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_69d85cce25008190b13b52745fbd719b |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e5f9db8819083abf80f01f32b3d |
completed | April 16, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff56ca72ec8190a237db843dc6d625 |
completed | May 9, 2026, 3:46 p.m. |
Created at: April 10, 2026, 4:12 a.m.