Triple
T5033310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margareta |
E113357
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Margita |
E113357
|
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: Margita | Statement: [Margareta, relatedName, Margita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Margita Context triple: [Margareta, relatedName, Margita]
-
A.
Marga
Marga is a feminine given name, commonly used as a short or diminutive form of names like Margarita or Margareta.
-
B.
Margareta
chosen
Margareta is a feminine given name used in various European languages, closely related to and derived from the name Margaret.
-
C.
Marida
Marida was the mother of the Abbasid caliph al-Mu'tasim, placing her within the influential familial circle of the Abbasid dynasty.
-
D.
Marta
Marta is a feminine given name commonly used in many European and Latin American countries, often considered a variant of the name Martha.
-
E.
Marta
Marta is a legendary Brazilian footballer widely regarded as one of the greatest women’s players of all time.
- 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_69bd443775e48190a646ffbfc4334723 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73b68d8c8190b8e04fb406abdb0f |
completed | March 20, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be9c71b2f081908c5c4c1d9ba1ccf4 |
completed | March 21, 2026, 1:26 p.m. |
Created at: March 20, 2026, 1:36 p.m.