Triple
T30085733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eugen Slutsky |
E764595
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object |
Slutsky theorem in probability theory
Slutsky theorem in probability theory is a fundamental result that describes how convergence in distribution and convergence in probability of random variables interact to determine the limiting behavior of their sums, products, and ratios.
|
E1900899
|
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: Slutsky theorem in probability theory | Statement: [Eugen Slutsky, knownFor, Slutsky theorem in probability theory]
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: Slutsky theorem in probability theory Triple: [Eugen Slutsky, knownFor, Slutsky theorem in probability theory]
Generated description
Slutsky theorem in probability theory is a fundamental result that describes how convergence in distribution and convergence in probability of random variables interact to determine the limiting behavior of their sums, products, and ratios.
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_69f22473c0fc8190a926a8051b3b378b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67d6c7874819094e666ddb8c1059f |
completed | May 2, 2026, 10:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a274ca053388190a71d413043b8f764 |
completed | June 8, 2026, 11:13 p.m. |
| NEDg | Description generation | batch_6a274da2b4f08190b54ffb23bd8b28dc |
completed | June 8, 2026, 11:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a274e7037d48190869592da30780fc0 |
completed | June 8, 2026, 11:21 p.m. |
Created at: April 29, 2026, 7:04 p.m.