Triple
T1910511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paulo Picasso |
E38098
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Paulo |
E180999
|
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: Paulo | Statement: [Paulo Picasso, givenName, Paulo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paulo Context triple: [Paulo Picasso, givenName, Paulo]
-
A.
Paulo
chosen
Paulo is the central character in Paulo Coelho’s novel "The Valkyries," whose spiritual journey through the Mojave Desert explores themes of faith, love, and self-discovery.
-
B.
Paolo
Paolo is the Italian form of the given name Paul, commonly used in Italy and other Italian-speaking communities.
-
C.
Marcelo
Marcelo is a common Portuguese and Spanish given name, notably borne by figures such as Brazilian footballer Marcelo Vieira and former Portuguese Prime Minister Marcelo Caetano.
-
D.
Mateus
Mateus is a Portuguese surname commonly borne by individuals such as Rui Mateus.
-
E.
Pablo
Pablo is a given name, especially common in Spanish-speaking countries, that corresponds to the English name Paul.
- 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_69a8862a26088190aae5243695aeefc0 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb1b88db48190a9229a7416054a85 |
completed | March 7, 2026, 5:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adfbac4e18819081f6cfdf1cd7a03c |
completed | March 8, 2026, 10:43 p.m. |
Created at: March 4, 2026, 7:35 p.m.