Triple
T7868258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zita Johann |
E182672
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Johann |
E27352
|
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: Johann | Statement: [Zita Johann, familyName, Johann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Johann Context triple: [Zita Johann, familyName, Johann]
-
A.
Johann
chosen
Johann is a given name of Germanic origin commonly used in German-speaking and other European countries.
-
B.
Johannes
Johannes is the given first name of Hubertus van Mook, a Dutch colonial administrator who served as Governor-General of the Dutch East Indies during and after World War II.
-
C.
Johannes
Johannes is a masculine given name of Hebrew origin, related to names like John and Johan and common in various European languages.
-
D.
Johannes
Johannes is the given first name of the German nuclear physicist Hans D. Jensen, a Nobel Prize laureate in Physics.
-
E.
Johannes
Johannes is the given name of Frederik Johannes Willem Reitz, a prominent South African lawyer, politician, and former State President of the Orange Free State.
- 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_69ca82894d9081908a832bfce71a4714 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3847c7fc819098e32b6548943da7 |
completed | March 31, 2026, 2:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5b60b3b08190832837bacb8ce965 |
completed | March 31, 2026, 5:28 a.m. |
Created at: March 30, 2026, 4:55 p.m.