Triple
T4105854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Sky Studios |
E88448
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Ferdinand |
E60224
|
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: Ferdinand | Statement: [Blue Sky Studios, notableWork, Ferdinand]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ferdinand Context triple: [Blue Sky Studios, notableWork, Ferdinand]
-
A.
Ferdinand
chosen
Ferdinand is a masculine given name of Germanic origin historically borne by numerous European nobles and monarchs.
-
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 is the given name of Fernando Primo de Rivera, a 19th-century Spanish general and politician who briefly served as Prime Minister of Spain.
-
D.
Fernando
Fernando is a masculine given name of Spanish and Portuguese origin, commonly used in many Spanish-speaking and Lusophone countries.
-
E.
Fernando
Fernando was the given name of the Duke of Alba who served as governor-general, a prominent Spanish noble and military leader.
- 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_69aed9484fb881909146f4c772ad277c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af019af25481909e9f1d171356f3e8 |
completed | March 9, 2026, 5:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b589d825c48190b4208b0502c257bb |
completed | March 14, 2026, 4:16 p.m. |
Created at: March 9, 2026, 3:40 p.m.