Triple
T15604328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carola Giedion-Welcker |
E375116
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Carola
Carola is a feminine given name used in various European languages, often considered a variant of Caroline or Carol.
|
E1166836
|
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: Carola | Statement: [Carola Giedion-Welcker, givenName, Carola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carola Context triple: [Carola Giedion-Welcker, givenName, Carola]
-
A.
Carola
Carola is a Dutch politician known for serving as Deputy Prime Minister and Minister of Agriculture, Nature and Food Quality in the Netherlands.
-
B.
Carola
Carola is a Swedish pop singer best known for winning the Eurovision Song Contest in 1991 with the song "Fångad av en stormvind."
-
C.
Lorena
Lorena is a city in the state of São Paulo, Brazil, known for hosting a campus of the University of São Paulo.
-
D.
Winona
Winona is a historic river city in southeastern Minnesota known for its Mississippi River bluffs, cultural festivals, and regional educational institutions.
-
E.
Tonina
Tonina is an ancient Maya archaeological site in Chiapas, Mexico, known for its towering acropolis, intricate relief sculptures, and significant role in Classic-period Maya politics.
- 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: Carola Triple: [Carola Giedion-Welcker, givenName, Carola]
Generated description
Carola is a feminine given name used in various European languages, often considered a variant of Caroline or Carol.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Carola Target entity description: Carola is a feminine given name used in various European languages, often considered a variant of Caroline or Carol.
-
A.
Carola
Carola is a Dutch politician known for serving as Deputy Prime Minister and Minister of Agriculture, Nature and Food Quality in the Netherlands.
-
B.
Carola
Carola is a Swedish pop singer best known for winning the Eurovision Song Contest in 1991 with the song "Fångad av en stormvind."
-
C.
Lorena
Lorena is a city in the state of São Paulo, Brazil, known for hosting a campus of the University of São Paulo.
-
D.
Winona
Winona is a historic river city in southeastern Minnesota known for its Mississippi River bluffs, cultural festivals, and regional educational institutions.
-
E.
Tonina
Tonina is an ancient Maya archaeological site in Chiapas, Mexico, known for its towering acropolis, intricate relief sculptures, and significant role in Classic-period Maya politics.
- 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_69d85cce25008190b13b52745fbd719b |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e7d9328819090e93d55881269a5 |
completed | April 16, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff56d1607c8190a42dc664abe45b32 |
completed | May 9, 2026, 3:46 p.m. |
| NEDg | Description generation | batch_69ff57c304188190afa695ae88cf0234 |
completed | May 9, 2026, 3:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff5920436c81909addad5bb4566ae9 |
completed | May 9, 2026, 3:56 p.m. |
Created at: April 10, 2026, 4:12 a.m.