Triple
T8055895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melania Knauss |
E187998
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Melania Knauss |
E187998
|
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: Melania Knauss | Statement: [Melania Knauss, name, Melania Knauss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Melania Knauss Context triple: [Melania Knauss, name, Melania Knauss]
-
A.
Melania Knauss
chosen
Melania Knauss is a Slovenian-American former fashion model best known as the wife of Donald Trump and former First Lady of the United States.
-
B.
Dimitra Priebus
Dimitra Priebus is the mother of American attorney and former White House Chief of Staff Reince Priebus.
-
C.
Barbara Müller
Barbara Müller was the wife of renowned German astronomer Johannes Kepler, with whom she shared a life during the early 17th century.
-
D.
Marianne Strauß
Marianne Strauß was the wife of influential Bavarian politician and long-time CSU leader Franz Josef Strauß.
-
E.
Pamela Habibović
Pamela Habibović is a Dutch scientist and academic leader who serves as rector magnificus of Maastricht University.
- 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_69ca82b2f68881908c50560697e210da |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3fa16804819094926ff2ff053c80 |
completed | March 31, 2026, 3:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc5725c5308190b2eb565902124b10 |
completed | March 31, 2026, 11:22 p.m. |
Created at: March 30, 2026, 5:25 p.m.