Triple
T9778357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flavia |
E237301
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Flavio |
E265851
|
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: Flavio | Statement: [Flavia, relatedName, Flavio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Flavio Context triple: [Flavia, relatedName, Flavio]
-
A.
Flavio
chosen
Flavio is a powerful photo-essay by Gordon Parks that documents the life of a young boy living in poverty in a Rio de Janeiro favela.
-
B.
Flavio Labiano
Flavio Labiano is a Spanish cinematographer known for his dynamic visual style on major action and adventure films, including Hollywood studio productions.
-
C.
Giulio
Giulio is the given name of Giulio Douhet, an influential early 20th-century Italian air power theorist and general.
-
D.
Lucio
Lucio is a Brazilian central defender renowned for his commanding presence, leadership, and success with both the Brazilian national team and top European clubs such as Bayern Munich and Inter Milan.
-
E.
Lucio
Lucio is a popular support hero in the game Overwatch, known for his music-based abilities that heal and speed up teammates.
- 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_69ca84d975a08190aab25b02a89bdab3 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda13545808190a47544b0cc666e20 |
completed | April 1, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1bd22a194819089ae888e932566f1 |
completed | April 5, 2026, 1:38 a.m. |
Created at: March 30, 2026, 8:26 p.m.