Triple
T3309286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Carlos, Prince of Asturias |
E69530
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Carlos |
E55653
|
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: Carlos | Statement: [Don Carlos, Prince of Asturias, givenName, Carlos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carlos Context triple: [Don Carlos, Prince of Asturias, givenName, Carlos]
-
A.
Carlos
chosen
Carlos is a common Spanish given name widely used across Spanish-speaking countries and communities.
-
B.
Manuel
Manuel is the given name of Manny Ramirez, the former Major League Baseball star known for his powerful hitting and tenure with the Boston Red Sox.
-
C.
Alfonso
Alfonso is a masculine given name of Spanish and Italian origin historically borne by numerous kings, nobles, and notable figures across Europe.
-
D.
Fernando
"Fernando" is a popular 1976 ballad by Swedish pop group ABBA, known for its nostalgic, storytelling lyrics and melodic harmonies.
-
E.
Fernando
Fernando is the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
- 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_69ad859f218081909458d2cebbf57565 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb0e9f33c81909cff835a83e0a657 |
completed | March 8, 2026, 5:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2f3edc0c081908a7f5c02fe18584b |
completed | March 12, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:11 p.m.