Triple
T9606984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steve Forbes |
E231995
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Sabina Beekman
Sabina Beekman is the wife of American publishing executive and former presidential candidate Steve Forbes.
|
E809935
|
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: Sabina Beekman | Statement: [Steve Forbes, spouse, Sabina Beekman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sabina Beekman Context triple: [Steve Forbes, spouse, Sabina Beekman]
-
A.
Wivina Demeester
Wivina Demeester is a Belgian politician known for her long-standing role in Flemish and national politics, particularly in public finance and infrastructure.
-
B.
Astrid Nienhuis
Astrid Nienhuis is a Dutch politician who serves as the mayor of the municipality of Heemstede in the Netherlands.
-
C.
Simone Buitendijk
Simone Buitendijk is a Dutch academic leader and scholar in higher education policy who has served as vice-chancellor of the University of Leeds.
-
D.
Lotte Verbeek
Lotte Verbeek is a Dutch actress and model best known internationally for her roles in historical drama series such as The Borgias and Outlander.
-
E.
Saskia de Jonge
Saskia de Jonge is a Dutch former competitive swimmer who specialized in freestyle events and represented the Netherlands in international competitions, including the Olympic Games.
- 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: Sabina Beekman Triple: [Steve Forbes, spouse, Sabina Beekman]
Generated description
Sabina Beekman is the wife of American publishing executive and former presidential candidate Steve Forbes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sabina Beekman Target entity description: Sabina Beekman is the wife of American publishing executive and former presidential candidate Steve Forbes.
-
A.
Wivina Demeester
Wivina Demeester is a Belgian politician known for her long-standing role in Flemish and national politics, particularly in public finance and infrastructure.
-
B.
Astrid Nienhuis
Astrid Nienhuis is a Dutch politician who serves as the mayor of the municipality of Heemstede in the Netherlands.
-
C.
Simone Buitendijk
Simone Buitendijk is a Dutch academic leader and scholar in higher education policy who has served as vice-chancellor of the University of Leeds.
-
D.
Lotte Verbeek
Lotte Verbeek is a Dutch actress and model best known internationally for her roles in historical drama series such as The Borgias and Outlander.
-
E.
Saskia de Jonge
Saskia de Jonge is a Dutch former competitive swimmer who specialized in freestyle events and represented the Netherlands in international competitions, including the Olympic Games.
- 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_69ca8485a90c819094fe40b42fde9d70 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a62372881908bf21be91e7285fb |
completed | April 1, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d17942504481908e7147a0f56bdf96 |
completed | April 4, 2026, 8:49 p.m. |
| NEDg | Description generation | batch_69d17a27596081909c6a2ec486480ce1 |
completed | April 4, 2026, 8:52 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d17af1d1b48190b6f8350edfa4f5ef |
completed | April 4, 2026, 8:56 p.m. |
Created at: March 30, 2026, 8:08 p.m.