Triple
T37585957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert J. Avrech |
E935108
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object |
Ariel Avrech
Ariel Avrech was the son of Emmy Award–winning screenwriter and Orthodox Jewish writer Robert J. Avrech, whose life and early death profoundly influenced his father's work and public writings.
|
E2246844
|
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: Ariel Avrech | Statement: [Robert J. Avrech, child, Ariel Avrech]
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: Ariel Avrech Triple: [Robert J. Avrech, child, Ariel Avrech]
Generated description
Ariel Avrech was the son of Emmy Award–winning screenwriter and Orthodox Jewish writer Robert J. Avrech, whose life and early death profoundly influenced his father's work and public writings.
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_69f76ece61dc8190a0ab33f8d87d0a7e |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fba88dda78819086e76736f0ffa8a7 |
completed | May 6, 2026, 8:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a4104070a248190a2cf133105f95882 |
completed | June 28, 2026, 11:22 a.m. |
| NEDg | Description generation | batch_6a4104e29abc8190826d9ac7d1dbe4c9 |
completed | June 28, 2026, 11:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a4106277e448190bf31165fd8f64020 |
completed | June 28, 2026, 11:31 a.m. |
Created at: May 3, 2026, 4:17 p.m.