Triple
T9371819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stella |
E225546
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Estella |
E196540
|
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: Estella | Statement: [Stella, hasVariant, Estella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Estella Context triple: [Stella, hasVariant, Estella]
-
A.
Estella
chosen
Estella is a historic town in northern Spain, renowned as an important stop on the Camino de Santiago pilgrimage route and for its well-preserved medieval architecture.
-
B.
Tarazona
Tarazona is a historic town in northeastern Spain known for its well-preserved Mudejar architecture and scenic setting near the Moncayo massif.
-
C.
Aramburu
Aramburu is a Spanish-language surname of Basque origin borne by various notable figures in politics, religion, and sports.
-
D.
Gracia Querejeta
Gracia Querejeta is a Spanish film director and screenwriter known for her character-driven dramas and significant contributions to contemporary Spanish cinema.
-
E.
Begoña
Begoña is a Spanish feminine given name commonly used in Spain and Spanish-speaking countries.
- 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_69ca842cbddc819099d71ecec48cf9e5 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd5083cbb8819088e8cef26be1b380 |
completed | April 1, 2026, 5:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0f41a6ca4819092bae8640136e964 |
completed | April 4, 2026, 11:20 a.m. |
Created at: March 30, 2026, 7:43 p.m.