Triple
T718710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claudia |
E14367
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Cláudia |
E14367
|
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: Cláudia | Statement: [Claudia, hasVariant, Cláudia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cláudia Context triple: [Claudia, hasVariant, Cláudia]
-
A.
Claudia
chosen
Claudia is a feminine given name used in various cultures, derived from the ancient Roman family name Claudius.
-
B.
Amada Cruz
Amada Cruz is an American museum director and arts administrator known for leading major art institutions, including serving as director of the Seattle Art Museum.
-
C.
Clara Rivera
Clara Rivera is the wife of Hall of Fame New York Yankees closer Mariano Rivera and is known for her involvement in charitable and community activities alongside him.
-
D.
Claudia Castello
Claudia Castello is a Brazilian film editor best known for her work on major feature films including the boxing drama "Creed."
-
E.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
- 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_69a4934a36e081909e7abef98b898a4e |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a58d4c3c8190ad4527d14bca5e6e |
completed | March 1, 2026, 8:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7a3a8bcc8819091c785ad953ddc54 |
completed | March 4, 2026, 3:14 a.m. |
Created at: March 1, 2026, 7:37 p.m.