Triple
T3183936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margarita |
E66654
|
entity |
| Predicate | hasVariantForm |
P457
|
FINISHED |
| Object | Margarida |
E114896
|
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: Margarida | Statement: [Margarita, hasVariantForm, Margarida]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Margarida Context triple: [Margarita, hasVariantForm, Margarida]
-
A.
Margarida
chosen
Margarida is a given name, commonly used in Portuguese and Catalan, that corresponds to the English name Margaret.
-
B.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
C.
Ricarda
Ricarda is a feminine given name, primarily used in German- and Spanish-speaking countries, derived from the male name Richard.
-
D.
Gertrudis
Gertrudis is a passionate and rebellious sister in "Like Water for Chocolate" whose fiery nature and unconventional choices challenge her family's strict traditions.
-
E.
María
"María" is a film featuring actress Taryn Power in a significant role.
- 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_69ad8587c1bc8190a2595f2c22ee1001 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada6bfc4248190af320471688c60f0 |
completed | March 8, 2026, 4:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b24b777fe881909764e2f2cdb68479 |
completed | March 12, 2026, 5:13 a.m. |
Created at: March 8, 2026, 3:06 p.m.