Triple
T26767282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jürg Gutknecht |
E674973
|
entity |
| Predicate | affiliation |
P10
|
FINISHED |
| Object |
Institute for Computer Systems at ETH Zurich
The Institute for Computer Systems at ETH Zurich is a research and teaching institute within ETH Zurich’s Department of Computer Science, focusing on the design, analysis, and implementation of computer systems and related technologies.
|
E1742364
|
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: Institute for Computer Systems at ETH Zurich | Statement: [Jürg Gutknecht, affiliation, Institute for Computer Systems at ETH Zurich]
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: Institute for Computer Systems at ETH Zurich Triple: [Jürg Gutknecht, affiliation, Institute for Computer Systems at ETH Zurich]
Generated description
The Institute for Computer Systems at ETH Zurich is a research and teaching institute within ETH Zurich’s Department of Computer Science, focusing on the design, analysis, and implementation of computer systems and related technologies.
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_69eecda85298819097ee1c38a3d772e7 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f6192a12ac8190a92158e548f4d5c8 |
completed | May 2, 2026, 3:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12095051ac8190a80b4ce38366e990 |
completed | May 23, 2026, 8:08 p.m. |
| NEDg | Description generation | batch_6a120a6fdd60819090c963c9a5577186 |
completed | May 23, 2026, 8:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a120b39cddc8190a6c89274fc238b1a |
completed | May 23, 2026, 8:16 p.m. |
Created at: April 27, 2026, 4 a.m.