Triple
T9853765
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haas |
E239533
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object |
Haass
Haass is a surname most notably associated with Richard N. Haass, an American diplomat and longtime president of the Council on Foreign Relations.
|
E825047
|
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: Haass | Statement: [Haas, hasVariant, Haass]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haass Context triple: [Haas, hasVariant, Haass]
-
A.
Konrad Haase
Konrad Haase was a German military officer who commanded defending forces during the World War II Dieppe Raid in 1942.
-
B.
Hansi
Hansi is a historic town in the Hisar district of Haryana, India, known for its ancient forts and archaeological significance.
-
C.
Wesselmann
Wesselmann is a surname most notably associated with Tom Wesselmann, a prominent American Pop Art painter known for his bold, stylized depictions of the nude and everyday consumer objects.
-
D.
Estermann
Estermann is a surname most notably associated with mathematician Theodor Estermann, known for his contributions to analytic number theory.
-
E.
Hugo Speer
Hugo Speer is an English actor best known for his role in the hit British comedy film "The Full Monty" and for numerous appearances in television dramas.
- 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: Haass Triple: [Haas, hasVariant, Haass]
Generated description
Haass is a surname most notably associated with Richard N. Haass, an American diplomat and longtime president of the Council on Foreign Relations.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Haass Target entity description: Haass is a surname most notably associated with Richard N. Haass, an American diplomat and longtime president of the Council on Foreign Relations.
-
A.
Konrad Haase
Konrad Haase was a German military officer who commanded defending forces during the World War II Dieppe Raid in 1942.
-
B.
Hansi
Hansi is a historic town in the Hisar district of Haryana, India, known for its ancient forts and archaeological significance.
-
C.
Wesselmann
Wesselmann is a surname most notably associated with Tom Wesselmann, a prominent American Pop Art painter known for his bold, stylized depictions of the nude and everyday consumer objects.
-
D.
Estermann
Estermann is a surname most notably associated with mathematician Theodor Estermann, known for his contributions to analytic number theory.
-
E.
Hugo Speer
Hugo Speer is an English actor best known for his role in the hit British comedy film "The Full Monty" and for numerous appearances in television dramas.
- 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_69ca84e4fdc08190a624425bcef98665 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb376d32c819089381cf6ed83629d |
completed | April 2, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d5f21a04819099f23ede55ec3417 |
completed | April 5, 2026, 3:24 a.m. |
| NEDg | Description generation | batch_69d1d7a6a87c81908dcd79c776bb19a1 |
completed | April 5, 2026, 3:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1d82007088190ac372c67a6760e65 |
completed | April 5, 2026, 3:33 a.m. |
Created at: March 30, 2026, 8:34 p.m.