Triple
T2259140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ferdinand de Lesseps |
E49996
|
entity |
| Predicate | givenName |
P17
|
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: [Ferdinand de Lesseps, givenName, Ferdinand]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ferdinand Context triple: [Ferdinand de Lesseps, givenName, 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 the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
-
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.
Leopoldo
Leopoldo is a masculine given name of Spanish and Italian origin, commonly used in Latin American and European countries.
-
E.
Carlos
Carlos is a common Spanish given name widely used across Spanish-speaking countries and communities.
- 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_69a88b01e0048190ba96431b5f990ba9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc15ad06c8190b6d0babc17015787 |
completed | March 7, 2026, 6:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae7f067f208190a399e2b1a83badd1 |
completed | March 9, 2026, 8:04 a.m. |
Created at: March 4, 2026, 7:48 p.m.