Triple
T16649590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pia |
E404566
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Pía |
E404566
|
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: Pía | Statement: [Pia, hasVariant, Pía]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pía Context triple: [Pia, hasVariant, Pía]
-
A.
Pia
Pia is a locality situated near the Agly River in southern France, known for its proximity to this waterway and its Mediterranean regional setting.
-
B.
Pia
chosen
Pia is a feminine given name used in various cultures, often derived from Latin meaning "pious" or "devout."
-
C.
Pia Fidelis
Pia Fidelis is an honorific title meaning "dutiful and loyal," historically awarded to Roman military units such as legions for their steadfast allegiance.
-
D.
Rosana
Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
-
E.
Rosana
Rosana is a Brazilian professional footballer known for her successful international career and contributions to top women’s clubs, including Avaldsnes IL.
- 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_69d8838a41f08190b0c3f79c47df5078 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37ad85ec881909dc6a434a363dab1 |
completed | April 18, 2026, 12:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a008a2f1da48190a1a01dbe25f8f0e9 |
completed | May 10, 2026, 1:37 p.m. |
Created at: April 10, 2026, 5:18 a.m.