Triple
T5238387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moritz Schlick |
E118279
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Moritz |
E176784
|
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: Moritz | Statement: [Moritz Schlick, givenName, Moritz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moritz Context triple: [Moritz Schlick, givenName, Moritz]
-
A.
Moritz
chosen
Moritz is a masculine given name of German origin, commonly used in German-speaking countries.
-
B.
Philipp Moritz
Philipp Moritz is a researcher in machine learning and reinforcement learning, known for co-authoring influential work such as the Proximal Policy Optimization (PPO) algorithm.
-
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 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.
-
E.
Johann
Johann is a given name of Germanic origin commonly used in German-speaking and other European countries.
- 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_69bd4467db0881909b3b0982df32cc8f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7b290b88819095bc99c234260d25 |
completed | March 20, 2026, 4:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69befe6323708190bfc95f01c65dc234 |
completed | March 21, 2026, 8:24 p.m. |
Created at: March 20, 2026, 1:49 p.m.