Triple
T34184671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Israel Meir Lau |
E876924
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Al Tishlach Yadcha El Ha-Na’ar
Al Tishlach Yadcha El Ha-Na’ar is a memoir by Rabbi Israel Meir Lau recounting his experiences as a child Holocaust survivor and his journey to becoming a leading Israeli rabbi.
|
E2084732
|
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: Al Tishlach Yadcha El Ha-Na’ar | Statement: [Israel Meir Lau, notableWork, Al Tishlach Yadcha El Ha-Na’ar]
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: Al Tishlach Yadcha El Ha-Na’ar Triple: [Israel Meir Lau, notableWork, Al Tishlach Yadcha El Ha-Na’ar]
Generated description
Al Tishlach Yadcha El Ha-Na’ar is a memoir by Rabbi Israel Meir Lau recounting his experiences as a child Holocaust survivor and his journey to becoming a leading Israeli rabbi.
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_69f349ae640c8190b9cd220b5368d8b6 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7100873e0819081941ca63dad0a0d |
completed | May 3, 2026, 9:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a36c1e3d0048190b4f989b1e17bcec0 |
completed | June 20, 2026, 4:37 p.m. |
| NEDg | Description generation | batch_6a36c27a91288190bf70c5526c0e9354 |
completed | June 20, 2026, 4:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a36c332a3a48190b0bfab6d38ef019a |
completed | June 20, 2026, 4:43 p.m. |
Created at: May 1, 2026, 1:55 a.m.