Triple
T37238892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tschirnhaus |
E923655
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Tschirnhaus transformation
The Tschirnhaus transformation is an algebraic method for simplifying polynomial equations by changing variables to eliminate certain terms, aiding in their solution.
|
E2218454
|
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: Tschirnhaus transformation | Statement: [Tschirnhaus, notableWork, Tschirnhaus transformation]
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: Tschirnhaus transformation Triple: [Tschirnhaus, notableWork, Tschirnhaus transformation]
Generated description
The Tschirnhaus transformation is an algebraic method for simplifying polynomial equations by changing variables to eliminate certain terms, aiding in their solution.
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_69f76ea9fee88190a589f661d95a7189 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb36d22d2c81909d777612e62b385f |
completed | May 6, 2026, 12:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a4043c736ac81909f9b8edf361621ae |
completed | June 27, 2026, 9:42 p.m. |
| NEDg | Description generation | batch_6a404474c9148190a29659eab3553908 |
completed | June 27, 2026, 9:45 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a4044edf4348190a0441a556da6d4eb |
completed | June 27, 2026, 9:47 p.m. |
Created at: May 3, 2026, 4:15 p.m.