Triple
T964257
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ferdinand von Lindemann |
E20803
|
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 von Lindemann, givenName, Ferdinand]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ferdinand Context triple: [Ferdinand von Lindemann, givenName, Ferdinand]
-
A.
Ferdinand
chosen
Ferdinand is a masculine given name of Germanic origin historically borne by numerous European nobles and monarchs.
-
B.
Carlos
Carlos is a common Spanish given name widely used across Spanish-speaking countries and communities.
-
C.
Guillermo
Guillermo is the Spanish form of the given name William, commonly used in Spanish-speaking countries.
-
D.
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.
-
E.
Luís
Luís is a common Portuguese male given name, historically associated with notable figures such as the poet Luís de Camões.
- 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b4303e5881909d101d11f9732c75 |
completed | March 1, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac89f641c48190b7cff1073852a228 |
completed | March 7, 2026, 8:26 p.m. |
Created at: March 1, 2026, 7:40 p.m.