Triple
T4496687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luís |
E100712
|
entity |
| Predicate | orthographicVariantUsedIn |
P33995
|
FINISHED |
| Object | Brazil (Luiz) |
—
|
LITERAL 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: Brazil (Luiz) | Statement: [Luís, orthographicVariantUsedIn, Brazil (Luiz)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: orthographicVariantUsedIn Context triple: [Luís, orthographicVariantUsedIn, Brazil (Luiz)]
-
A.
orthographicVariant
chosen
Indicates that two written forms are different spellings or orthographic representations of the same linguistic item.
-
B.
hasOrthographicReform
Indicates that an entity has undergone or is associated with a change or standardization in its writing system or spelling conventions.
-
C.
orthographicProperty
Indicates a relationship where a specific written or spelling-related characteristic is attributed to or associated with an entity.
-
D.
usesStandardOrthographyOf
Indicates that one entity writes or represents language according to the standard orthographic system defined for another entity.
-
E.
hasStandardOrthographySince
Indicates that a language or writing system has used a particular standardized orthography starting from a specified point in time.
- F. None of above.
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_69bd43cdf15081909a4fa2585ff63b3e |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd56bf3ff48190b3aae0136d7fce45 |
completed | March 20, 2026, 2:16 p.m. |
| PD | Predicate disambiguation | batch_69bd521671688190bc655d25fa77eba2 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1 p.m.