Triple
T12556349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fernanda |
E295226
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object |
Fernando
Fernando is a given name commonly used in Spanish- and Portuguese-speaking countries, equivalent to the English name Ferdinand.
|
E410234
|
NE FINISHED |
How this triple was built (4 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: Fernando | Statement: [Fernanda, relatedName, Fernando]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fernando Context triple: [Fernanda, relatedName, Fernando]
-
A.
Fernando
Fernando is the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
-
B.
Fernando
"Fernando" is a popular 1976 ballad by Swedish pop group ABBA, known for its nostalgic, storytelling lyrics and melodic harmonies.
-
C.
Fernando
Fernando was the given name of the Duke of Alba who served as governor-general, a prominent Spanish noble and military leader.
-
D.
Fernando
Fernando is the given name of Fernando Primo de Rivera, a 19th-century Spanish general and politician who briefly served as Prime Minister of Spain.
-
E.
Fernando
Fernando is a masculine given name of Spanish and Portuguese origin, commonly used in many Spanish-speaking and Lusophone countries.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Fernando Triple: [Fernanda, relatedName, Fernando]
Generated description
Fernando is a given name commonly used in Spanish- and Portuguese-speaking countries, equivalent to the English name Ferdinand.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fernando Target entity description: Fernando is a given name commonly used in Spanish- and Portuguese-speaking countries, equivalent to the English name Ferdinand.
-
A.
Fernando
Fernando is the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
-
B.
Fernando
"Fernando" is a popular 1976 ballad by Swedish pop group ABBA, known for its nostalgic, storytelling lyrics and melodic harmonies.
-
C.
Fernando
Fernando was the given name of the Duke of Alba who served as governor-general, a prominent Spanish noble and military leader.
-
D.
Fernando
Fernando is the given name of Fernando Primo de Rivera, a 19th-century Spanish general and politician who briefly served as Prime Minister of Spain.
-
E.
Fernando
chosen
Fernando is a masculine given name of Spanish and Portuguese origin, commonly used in many Spanish-speaking and Lusophone countries.
- F. None of above.
Provenance (5 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_69d6ad9cac2c81908e8a7bed82d1e21d |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95490d2708190857f0cb9b8dd6a30 |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69b847de481908163d59cc939e132 |
completed | May 3, 2026, 12:49 a.m. |
| NEDg | Description generation | batch_69f69c69bb608190a3fb27227cd742b1 |
completed | May 3, 2026, 12:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f69d4ef7988190890f8a62280aa673 |
completed | May 3, 2026, 12:56 a.m. |
Created at: April 8, 2026, 11:47 p.m.