Triple
T31769261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Foundation for Polish Science Prize |
E810891
|
entity |
| Predicate | notableLaureate |
P1618
|
FINISHED |
| Object |
Henryk Iwaniec
Henryk Iwaniec is a prominent Polish-American mathematician renowned for his influential work in analytic number theory and prime number distribution.
|
E1975235
|
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: Henryk Iwaniec | Statement: [Foundation for Polish Science Prize, notableLaureate, Henryk Iwaniec]
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: Henryk Iwaniec Triple: [Foundation for Polish Science Prize, notableLaureate, Henryk Iwaniec]
Generated description
Henryk Iwaniec is a prominent Polish-American mathematician renowned for his influential work in analytic number theory and prime number distribution.
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_69f348e463e08190b902d4819195e1f0 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6abadf4408190a848007166af0a96 |
completed | May 3, 2026, 1:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2b9494816c8190a0c71e0c2c62b354 |
completed | June 12, 2026, 5:09 a.m. |
| NEDg | Description generation | batch_6a2b956f0e188190a0ff10a21ff836f3 |
completed | June 12, 2026, 5:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a2b9638b0608190bfad095c1c202f00 |
completed | June 12, 2026, 5:16 a.m. |
Created at: April 30, 2026, 11:33 p.m.