Triple
T6717427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mateo |
E153306
|
entity |
| Predicate | isCognateWith |
P2527
|
FINISHED |
| Object | Matheus |
E110964
|
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: Matheus | Statement: [Mateo, isCognateWith, Matheus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matheus Context triple: [Mateo, isCognateWith, Matheus]
-
A.
Mateus
chosen
Mateus is a Portuguese surname commonly borne by individuals such as Rui Mateus.
-
B.
Paulo
Paulo is a given name most notably associated with Brazilian educator and philosopher Paulo Freire, a leading figure in critical pedagogy.
-
C.
Paulo
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.
-
D.
Marcio
Marcio is a masculine given name commonly used in Portuguese- and Spanish-speaking countries, derived from the Latin name Marcius.
-
E.
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.
- 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_69c68809b4608190a2509ddb5ab87f05 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d12765a48190b485176dc2ffa0fa |
completed | March 27, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c700993128819081614ccfa68d7320 |
completed | March 27, 2026, 10:11 p.m. |
Created at: March 27, 2026, 2:07 p.m.