Triple
T312805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leonard Bernstein |
E7643
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Leonard
Leonard is a masculine given name of Germanic origin, commonly used in English-speaking countries and borne by numerous notable figures in arts, sports, and public life.
|
E53541
|
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: Leonard | Statement: [Leonard Bernstein, givenName, Leonard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leonard Context triple: [Leonard Bernstein, givenName, Leonard]
-
A.
Laurence
Laurence is a masculine given name of Latin origin, commonly used in English-speaking countries.
-
B.
Lester
Lester is the given name of Lester B. Pearson, the Canadian diplomat, Nobel Peace Prize laureate, and 14th prime minister of Canada.
-
C.
Harold
Harold is a masculine given name of Old English origin, historically borne by several notable figures including kings and modern public personalities.
-
D.
Walter
Walter is a masculine given name of Germanic origin that has been widely used in English-speaking countries.
-
E.
Bernard
Bernard is a masculine given name of Old French and Germanic origin, historically borne by notable figures such as military leaders and saints.
- 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: Leonard Triple: [Leonard Bernstein, givenName, Leonard]
Generated description
Leonard is a masculine given name of Germanic origin, commonly used in English-speaking countries and borne by numerous notable figures in arts, sports, and public life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Leonard Target entity description: Leonard is a masculine given name of Germanic origin, commonly used in English-speaking countries and borne by numerous notable figures in arts, sports, and public life.
-
A.
Laurence
Laurence is a masculine given name of Latin origin, commonly used in English-speaking countries.
-
B.
Lester
Lester is the given name of Lester B. Pearson, the Canadian diplomat, Nobel Peace Prize laureate, and 14th prime minister of Canada.
-
C.
Harold
Harold is a masculine given name of Old English origin, historically borne by several notable figures including kings and modern public personalities.
-
D.
Walter
Walter is a masculine given name of Germanic origin that has been widely used in English-speaking countries.
-
E.
Bernard
Bernard is a masculine given name of Old French and Germanic origin, historically borne by notable figures such as military leaders and saints.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea4aa16881909b2c8404b85992df |
completed | Feb. 28, 2026, 1:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a42a101a908190808a4e10871b357d |
completed | March 1, 2026, 11:59 a.m. |
| NEDg | Description generation | batch_69a42a588af08190bd59fee1d9c22021 |
completed | March 1, 2026, noon |
| NED2 | Entity disambiguation (via description) | batch_69a42aac27688190958466bba645b265 |
completed | March 1, 2026, 12:01 p.m. |
Created at: Feb. 28, 2026, 1:07 p.m.