Triple
T3866130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Friedrich Robert Faehlmann |
E91858
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Friedrich |
E66781
|
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: Friedrich | Statement: [Friedrich Robert Faehlmann, givenName, Friedrich]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Friedrich Context triple: [Friedrich Robert Faehlmann, givenName, Friedrich]
-
A.
Friedrich
Friedrich is the given first name of the renowned German mathematician Bernhard Riemann.
-
B.
Friedrich
Friedrich is the given first name of the German philosopher, logician, and mathematician Gottlob Frege.
-
C.
Friedrich
Friedrich is the given name of Friedrich Engels, the 19th-century German philosopher, social scientist, and co-author of key works on communism alongside Karl Marx.
-
D.
Friedrich
chosen
Friedrich is a German masculine given name of Old High German origin, commonly borne by historical figures and nobility.
-
E.
Friedrich
Friedrich is a German soldier in Michael Morpurgo’s novel "War Horse," known for his compassionate treatment of horses amid the brutality of World War I.
- 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_69aed9645f348190a9868e7cef56ab7e |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec3b8d988190b56d42ac1521e19c |
completed | March 9, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b589bcbce88190b97b9dcec8a976f4 |
completed | March 14, 2026, 4:15 p.m. |
Created at: March 9, 2026, 3:19 p.m.