Triple
T6460939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arana |
E142118
|
entity |
| Predicate | notableBearer |
P458
|
FINISHED |
| Object | Tomas Arana |
E26756
|
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: Tomas Arana | Statement: [Arana, notableBearer, Tomas Arana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tomas Arana Context triple: [Arana, notableBearer, Tomas Arana]
-
A.
Tomas Arana
chosen
Tomas Arana is an American actor known for his supporting roles in films such as "Gladiator," "The Hunt for Red October," and "The Dark Knight Rises."
-
B.
Eduardo Arenas
Eduardo Arenas is a notable individual recognized for achievements significant enough to be distinctly associated with the surname Arenas.
-
C.
Rafael Arenas
Rafael Arenas is an individual notable enough to be recognized as a prominent bearer of the surname Arenas.
-
D.
Roberto Conesa
Roberto Conesa was a notorious Spanish police commissioner and secret police operative under Franco’s dictatorship, known for his role in political repression and torture of regime opponents.
-
E.
Pablo Batalla
Pablo Batalla is an Argentine attacking midfielder best known for his influential spell at Turkish club Bursaspor, where he became a key playmaker and fan favorite.
- 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_69c008d2f91c8190a8178767a35e08fc |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c069f683248190a58beb60f009eafb |
completed | March 22, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cb02cb3c8190917842bb654326ee |
completed | March 27, 2026, 6:22 p.m. |
Created at: March 22, 2026, 4:48 p.m.