Triple
T274165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black |
E5209
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Sharon Black
Sharon Black is a notable individual whose achievements or public presence have made the surname Black recognizable in her context.
|
E99454
|
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: Sharon Black | Statement: [Black, hasNotableBearer, Sharon Black]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sharon Black Context triple: [Black, hasNotableBearer, Sharon Black]
-
A.
Sharon Meadow
Sharon Meadow is a popular open grassy area in San Francisco’s Golden Gate Park often used for picnics, festivals, and outdoor events.
-
B.
Melinda Rogers
Melinda Rogers is a Canadian business executive and member of the Rogers family, known for her leadership roles within Rogers Communications.
-
C.
Margo Anderson
Margo Anderson is best known as a former wife of American country music star Kenny Rogers.
-
D.
Deborah Prentice
Deborah Prentice is an American social psychologist and academic leader known for her work on social norms and for serving as Vice-Chancellor of the University of Cambridge.
-
E.
Marla Maples
Marla Maples is an American actress and television personality best known for her high-profile marriage to businessman and future U.S. President Donald Trump in the 1990s.
- 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: Sharon Black Triple: [Black, hasNotableBearer, Sharon Black]
Generated description
Sharon Black is a notable individual whose achievements or public presence have made the surname Black recognizable in her context.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sharon Black Target entity description: Sharon Black is a notable individual whose achievements or public presence have made the surname Black recognizable in her context.
-
A.
Sharon Meadow
Sharon Meadow is a popular open grassy area in San Francisco’s Golden Gate Park often used for picnics, festivals, and outdoor events.
-
B.
Melinda Rogers
Melinda Rogers is a Canadian business executive and member of the Rogers family, known for her leadership roles within Rogers Communications.
-
C.
Margo Anderson
Margo Anderson is best known as a former wife of American country music star Kenny Rogers.
-
D.
Deborah Prentice
Deborah Prentice is an American social psychologist and academic leader known for her work on social norms and for serving as Vice-Chancellor of the University of Cambridge.
-
E.
Marla Maples
Marla Maples is an American actress and television personality best known for her high-profile marriage to businessman and future U.S. President Donald Trump in the 1990s.
- 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_69a257e6c8788190987dfe705ca2912a |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25dd0a99c819089968a5400c58c5f |
completed | Feb. 28, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a79273925c8190aba320775ef88061 |
completed | March 4, 2026, 2:01 a.m. |
| NEDg | Description generation | batch_69a792fcb75881908562b79005d62b3b |
completed | March 4, 2026, 2:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a793fd988481908ad3c6d2cd46026d |
completed | March 4, 2026, 2:07 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.