Triple
T4142056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gerard ’t Hooft |
E89292
|
entity |
| Predicate | doctoralStudent |
P167
|
FINISHED |
| Object |
Jan Smit
Jan Smit is a theoretical physicist known for his work in lattice gauge theory and contributions to the foundations of quantum field theory.
|
E415086
|
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: Jan Smit | Statement: [Gerard ’t Hooft, doctoralStudent, Jan Smit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jan Smit Context triple: [Gerard ’t Hooft, doctoralStudent, Jan Smit]
-
A.
Ben Louw
Ben Louw is an individual notable enough to be specifically cited as a prominent bearer of the surname Louw.
-
B.
Jeroen Krabbé
Jeroen Krabbé is a Dutch actor and director known internationally for his roles in films such as "The Living Daylights," "The Fugitive," and "Ocean's Twelve."
-
C.
Marc de Jonge
Marc de Jonge was a French actor best known internationally for playing the Soviet Colonel Zaysen in the action film "Rambo III."
-
D.
Christian Huitema
Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
-
E.
Theo de Meester
Theo de Meester was a Dutch liberal politician who served as Prime Minister of the Netherlands in the early 20th century.
- 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: Jan Smit Triple: [Gerard ’t Hooft, doctoralStudent, Jan Smit]
Generated description
Jan Smit is a theoretical physicist known for his work in lattice gauge theory and contributions to the foundations of quantum field theory.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jan Smit Target entity description: Jan Smit is a theoretical physicist known for his work in lattice gauge theory and contributions to the foundations of quantum field theory.
-
A.
Ben Louw
Ben Louw is an individual notable enough to be specifically cited as a prominent bearer of the surname Louw.
-
B.
Jeroen Krabbé
Jeroen Krabbé is a Dutch actor and director known internationally for his roles in films such as "The Living Daylights," "The Fugitive," and "Ocean's Twelve."
-
C.
Marc de Jonge
Marc de Jonge was a French actor best known internationally for playing the Soviet Colonel Zaysen in the action film "Rambo III."
-
D.
Christian Huitema
Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
-
E.
Theo de Meester
Theo de Meester was a Dutch liberal politician who served as Prime Minister of the Netherlands in the early 20th century.
- 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_69aed95785788190ae75bcf0cd1cafdf |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af024b8fe4819098e8f393474363c8 |
completed | March 9, 2026, 5:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b576cff6c881909134804ba6f9876d |
completed | March 14, 2026, 2:55 p.m. |
| NEDg | Description generation | batch_69b577d391ac8190b6062b1f64e2e7e8 |
completed | March 14, 2026, 2:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5787ed214819092fc425152069df9 |
completed | March 14, 2026, 3:02 p.m. |
Created at: March 9, 2026, 3:43 p.m.