Triple
T37096898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sara Ben-Artzi |
E918586
|
entity |
| Predicate | sibling |
P363
|
FINISHED |
| Object |
Hagai Ben-Artzi
Hagai Ben-Artzi is an Israeli mathematician and academic, known for his work in analysis and partial differential equations.
|
E2221801
|
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: Hagai Ben-Artzi | Statement: [Sara Ben-Artzi, sibling, Hagai Ben-Artzi]
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: Hagai Ben-Artzi Triple: [Sara Ben-Artzi, sibling, Hagai Ben-Artzi]
Generated description
Hagai Ben-Artzi is an Israeli mathematician and academic, known for his work in analysis and partial differential equations.
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_69f76e9a48bc8190a3947508d8bca408 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb2fd5274081909e9537df3c86d42b |
completed | May 6, 2026, 12:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a40637503ec8190b54abd860ef00717 |
completed | June 27, 2026, 11:57 p.m. |
| NEDg | Description generation | batch_6a4064b46ae48190b0949d72795badd6 |
completed | June 28, 2026, 12:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a40651995508190a458b790a90bd3aa |
completed | June 28, 2026, 12:04 a.m. |
Created at: May 3, 2026, 4:14 p.m.