Triple
T16784663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moshe Alshich |
E407940
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object |
HaAlshich
HaAlshich is the honorific title of Rabbi Moshe Alshich, a prominent 16th-century Torah commentator and kabbalist from Safed.
|
E1234292
|
NE FINISHED |
How this triple was built (4 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: HaAlshich | Statement: [Moshe Alshich, name, HaAlshich]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HaAlshich Context triple: [Moshe Alshich, name, HaAlshich]
-
A.
Ha-Shahar
Ha-Shahar was a 19th-century Hebrew-language periodical associated with the Jewish Enlightenment (Haskalah) movement.
-
B.
HaRosh
HaRosh, also known as the Rosh, was a prominent medieval rabbi and halachic authority whose legal rulings significantly shaped later Jewish law.
-
C.
Aheloy
Aheloy is a small Bulgarian coastal town on the Black Sea, known for its tranquil beaches and proximity to the larger resort areas of the Burgas region.
-
D.
Abba Hushi
Abba Hushi was an Israeli labor leader and politician who played a central role in developing Haifa into a major industrial and cultural center in the mid-20th century.
-
E.
Ha-Hov
Ha-Hov is an Israeli thriller film that follows Mossad agents confronting the consequences of a past mission to capture a Nazi war criminal.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: HaAlshich Triple: [Moshe Alshich, name, HaAlshich]
Generated description
HaAlshich is the honorific title of Rabbi Moshe Alshich, a prominent 16th-century Torah commentator and kabbalist from Safed.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: HaAlshich Target entity description: HaAlshich is the honorific title of Rabbi Moshe Alshich, a prominent 16th-century Torah commentator and kabbalist from Safed.
-
A.
Ha-Shahar
Ha-Shahar was a 19th-century Hebrew-language periodical associated with the Jewish Enlightenment (Haskalah) movement.
-
B.
HaRosh
HaRosh, also known as the Rosh, was a prominent medieval rabbi and halachic authority whose legal rulings significantly shaped later Jewish law.
-
C.
Aheloy
Aheloy is a small Bulgarian coastal town on the Black Sea, known for its tranquil beaches and proximity to the larger resort areas of the Burgas region.
-
D.
Abba Hushi
Abba Hushi was an Israeli labor leader and politician who played a central role in developing Haifa into a major industrial and cultural center in the mid-20th century.
-
E.
Ha-Hov
Ha-Hov is an Israeli thriller film that follows Mossad agents confronting the consequences of a past mission to capture a Nazi war criminal.
- F. None of above. chosen
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_69d8839270588190886720d9519bbf8f |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b21996cc81909deb88545af7079f |
completed | April 18, 2026, 4:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00ab056dc88190aeb9e135ae955125 |
completed | May 10, 2026, 3:57 p.m. |
| NEDg | Description generation | batch_6a00abe78194819098a2867c7cb5b8b2 |
completed | May 10, 2026, 4:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00afddb580819090e6b4e9dd4c1545 |
completed | May 10, 2026, 4:18 p.m. |
Created at: April 10, 2026, 5:22 a.m.