Triple
T9501070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gregorio |
E229139
|
entity |
| Predicate | hasCognate |
P2525
|
FINISHED |
| Object | Gregor (German) |
E194828
|
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: Gregor (German) | Statement: [Gregorio, hasCognate, Gregor (German)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gregor (German) Context triple: [Gregorio, hasCognate, Gregor (German)]
-
A.
Gregor
chosen
Gregor is a masculine given name of Latin origin, commonly associated with figures such as the pioneering geneticist Gregor Mendel.
-
B.
Gregor de Berghmann
Gregor de Berghmann is a fictional character appearing in the narrative of "The Black Room."
-
C.
Franz
Franz is the given name of Franz Cardinal König, a prominent 20th-century Austrian Catholic cardinal and influential church leader.
-
D.
Franz
Franz is a German-language surname of Central European origin borne by various notable individuals.
-
E.
Franz
Franz is a character in Louisa May Alcott's novel "Little Men," one of the boys at Plumfield School whose experiences reflect the book's themes of growth, education, and moral development.
- 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_69ca84753660819098e8d416e89e26ae |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd983d4b708190a4dfef1246986a26 |
completed | April 1, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d13a0a5ec881908bb1643d2bea2c9f |
completed | April 4, 2026, 4:19 p.m. |
Created at: March 30, 2026, 7:57 p.m.