Triple
T144161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johan |
E2916
|
entity |
| Predicate | hasSpellingVariant |
P457
|
FINISHED |
| Object |
Jóhan
Jóhan is a given name, primarily used in Faroese and other Nordic contexts, that corresponds to the name Johan.
|
E17355
|
NE FINISHED |
How this triple was built (4 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: Jóhan | Statement: [Johan, hasSpellingVariant, Jóhan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jóhan Context triple: [Johan, hasSpellingVariant, Jóhan]
-
A.
Audun Tron
Audun Tron is a Norwegian politician who served as the mayor of Lillehammer during the period when the city hosted the 1994 Winter Olympics.
-
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.
Peter Amundson
Peter Amundson is a film editor best known for his work on major Hollywood productions, including the science-fiction action film "Pacific Rim."
-
D.
Theodor
Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
-
E.
Andreas
Andreas is a masculine given name of Greek origin, commonly used in various European and international cultures.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Jóhan Triple: [Johan, hasSpellingVariant, Jóhan]
Generated description
Jóhan is a given name, primarily used in Faroese and other Nordic contexts, that corresponds to the name Johan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jóhan Target entity description: Jóhan is a given name, primarily used in Faroese and other Nordic contexts, that corresponds to the name Johan.
-
A.
Audun Tron
Audun Tron is a Norwegian politician who served as the mayor of Lillehammer during the period when the city hosted the 1994 Winter Olympics.
-
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.
Peter Amundson
Peter Amundson is a film editor best known for his work on major Hollywood productions, including the science-fiction action film "Pacific Rim."
-
D.
Theodor
Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
-
E.
Andreas
Andreas is a masculine given name of Greek origin, commonly used in various European and international cultures.
- F. None of above. chosen
Provenance (5 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_69a257e935bc8190a03e54a10e9ba6f7 |
completed | Feb. 28, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2c2763ce481908c12046de9003a84 |
completed | Feb. 28, 2026, 10:24 a.m. |
| NEDg | Description generation | batch_69a2c2f02810819092e3263ac91b5fe3 |
completed | Feb. 28, 2026, 10:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a2c369498481908c4213b04aea9c97 |
completed | Feb. 28, 2026, 10:28 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.