Triple
T144153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johan |
E2916
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Johannes |
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: Johannes | Statement: [Johan, relatedName, Johannes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Johannes Context triple: [Johan, relatedName, Johannes]
-
A.
Johann
chosen
Johann is a given name of Germanic origin commonly used in German-speaking and other European countries.
-
B.
Johan
Johan is the given first name of J. Erik Jonsson, an American businessman and philanthropist who co-founded Texas Instruments and served as mayor of Dallas.
-
C.
Willem
Willem is a given name, primarily used in Dutch-speaking regions, that corresponds to the English name William.
-
D.
Andreas
Andreas is a masculine given name of Greek origin, commonly used in various European and international cultures.
-
E.
Theodor
Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
- 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_69a2521e35c08190b28e5c9f1e3c9b59 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a25bab43608190ba5ebfbee6b5b6e4 |
completed | Feb. 28, 2026, 3:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a34418bb848190ac26adef7f990f93 |
completed | Feb. 28, 2026, 7:38 p.m. |
Created at: Feb. 28, 2026, 2:31 a.m.