Triple
T2925079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mikel Arteta |
E78823
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mikel |
E114789
|
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: Mikel | Statement: [Mikel Arteta, givenName, Mikel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mikel Context triple: [Mikel Arteta, givenName, Mikel]
-
A.
Mikel
chosen
Mikel is a given name, commonly a variant of Michael used in various cultures, particularly in Basque and Spanish-speaking regions.
-
B.
Álvaro
Álvaro is a masculine given name of Spanish origin commonly used in Spain and Latin America.
-
C.
Rubén
Rubén is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
-
D.
Javier
Javier is a masculine given name of Spanish origin commonly used in Spanish-speaking countries and beyond.
-
E.
Estévez
Estévez is the original Spanish family name of actor Martin Sheen, also shared by several of his children in the entertainment industry.
- 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_69ad8b0d40b481908bc2a5fa2e73c3fb |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad97c086888190ba51ce659a6c4f50 |
completed | March 8, 2026, 3:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b0563a81788190b94fab34e41a76e7 |
completed | March 10, 2026, 5:34 p.m. |
Created at: March 8, 2026, 2:55 p.m.