Triple
T1432622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lars Onsager |
E30483
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Lars
Lars is a masculine given name of Scandinavian origin, commonly used in countries such as Norway, Sweden, and Denmark.
|
E163910
|
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: Lars | Statement: [Lars Onsager, givenName, Lars]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lars Context triple: [Lars Onsager, givenName, Lars]
-
A.
Mikael
Mikael is a masculine given name commonly used in Scandinavian and Finnish cultures, equivalent to Michael.
-
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.
Morten
Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
-
D.
Lars Jensen
Lars Jensen is an entrepreneur best known as a co-founder of the online advertising technology company DoubleClick.
-
E.
Lars Johanson
Lars Johanson is a Swedish linguist renowned for his influential work on Turkic languages and language contact, and for his critical stance toward the proposed Altaic language family.
- 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: Lars Triple: [Lars Onsager, givenName, Lars]
Generated description
Lars is a masculine given name of Scandinavian origin, commonly used in countries such as Norway, Sweden, and Denmark.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lars Target entity description: Lars is a masculine given name of Scandinavian origin, commonly used in countries such as Norway, Sweden, and Denmark.
-
A.
Mikael
Mikael is a masculine given name commonly used in Scandinavian and Finnish cultures, equivalent to Michael.
-
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.
Morten
Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
-
D.
Lars Jensen
Lars Jensen is an entrepreneur best known as a co-founder of the online advertising technology company DoubleClick.
-
E.
Lars Johanson
Lars Johanson is a Swedish linguist renowned for his influential work on Turkic languages and language contact, and for his critical stance toward the proposed Altaic language family.
- 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_69a498fc69ec8190b61722bd4b67c4d2 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c4ddbe208190a68cb000a6970d17 |
completed | March 1, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad016e25808190880a6e637dd2590a |
completed | March 8, 2026, 4:56 a.m. |
| NEDg | Description generation | batch_69ad01f8028881909af95e9f61a17e88 |
completed | March 8, 2026, 4:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad02c618c48190abf3e16d9f85e703 |
completed | March 8, 2026, 5:01 a.m. |
Created at: March 1, 2026, 8 p.m.