Triple
T2882637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christiaan |
E59431
|
entity |
| Predicate | hasVariantSpelling |
P457
|
FINISHED |
| Object | Cristian |
E282593
|
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: Cristian | Statement: [Christiaan, hasVariantSpelling, Cristian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cristian Context triple: [Christiaan, hasVariantSpelling, Cristian]
-
A.
Cristian
chosen
Cristian is a masculine given name commonly used in various European and Latin American countries, often associated with Christian religious roots.
-
B.
Kristian
Kristian is a given name notably borne by Lauri Kristian Relander, the second President of Finland.
-
C.
Roberto
Roberto is a masculine given name commonly used in Romance-language countries, equivalent to the English name Robert.
-
D.
Michael Luciano
Michael Luciano is a film editor best known for his long collaboration with director Robert Aldrich, including work on the noir classic "Kiss Me Deadly."
-
E.
Cipriano
Cipriano is a masculine given name of Spanish origin, historically borne by figures such as the Protestant reformer and Bible translator Cipriano de Valera.
- 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_69ab4ac739188190a112f42a5a69c951 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abe02c238881908f7a349563c388bf |
completed | March 7, 2026, 8:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b03167d7dc819093e91e0d42f3de6f |
completed | March 10, 2026, 2:57 p.m. |
Created at: March 6, 2026, 10:03 p.m.