Triple
T21551990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Women's Professional Soccer 2009 season |
E531783
|
entity |
| Predicate | MVP |
P2630
|
FINISHED |
| Object | Marta |
—
|
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: Marta | Statement: [Women's Professional Soccer 2009 season, MVP, Marta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marta Context triple: [Women's Professional Soccer 2009 season, MVP, Marta]
-
A.
Marta
Marta is a feminine given name commonly used in many European and Latin American countries, often considered a variant of the name Martha.
-
B.
Marta
chosen
Marta is a legendary Brazilian footballer widely regarded as one of the greatest women’s players of all time.
-
C.
Marta
Marta is a small Italian town in the Lazio region, situated on the southern shore of Lake Bolsena and known for its lakeside scenery and historic center.
-
D.
Marcela
Marcela is one of the given names of Alexia Juliana Marcela Laurentien, a member of the Dutch royal family.
-
E.
Marcelina
Marcelina is a central character in Ian McDonald's science fiction novel "Brasyl," set within its intricate, multi-timeline narrative in Brazil.
- 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_69e0c460232c81908de2c3819d17c00e |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eeb59375f481909d9e2b66d18c7c32 |
completed | April 27, 2026, 1:02 a.m. |
Created at: April 16, 2026, 6:29 p.m.