Triple
T9816966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philipp Moritz Maria of Bavaria |
E238430
|
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: [Philipp Moritz Maria of Bavaria, givenName, Moritz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moritz Context triple: [Philipp Moritz Maria of Bavaria, 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.
Moritz Klaus
Moritz Klaus is a German actor best known internationally for his role in the 2022 anti-war film "All Quiet on the Western Front."
-
D.
Franz
Franz is the given name of Franz Cardinal König, a prominent 20th-century Austrian Catholic cardinal and influential church leader.
-
E.
Franz
Franz is a German-language surname of Central European origin borne by various notable individuals.
- 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_69ca84dfde1481909f47c286d715f892 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb2f4a1548190a5afc5ee0d7da392 |
completed | April 2, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d269b54d04819096ddc9f16a6db17b |
completed | April 5, 2026, 1:55 p.m. |
Created at: March 30, 2026, 8:30 p.m.