Triple
T13590702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hennessy |
E324684
|
entity |
| Predicate | notableProduct |
P1448
|
FINISHED |
| Object | Richard Hennessy |
E1049176
|
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: Richard Hennessy | Statement: [Hennessy, notableProduct, Richard Hennessy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Richard Hennessy Context triple: [Hennessy, notableProduct, Richard Hennessy]
-
A.
Richard Hennessy
chosen
Richard Hennessy was an Irish officer and entrepreneur who founded the renowned French cognac house Hennessy in the 18th century.
-
B.
George Hennessy
George Hennessy was a British Conservative politician who served as a Member of Parliament in the early 20th century.
-
C.
Day Hennessy
Day Hennessy is a surname associated with the individual Tamar Teresa Day Hennessy.
-
D.
David Hennessy
David Hennessy is an individual known primarily in relation to his marriage to Tamar Teresa Day Hennessy.
-
E.
William Grant Sherry
William Grant Sherry was an American artist and World War II veteran best known as the third husband of actress Bette Davis.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb055cc98819091fab597b69e5e3e |
completed | April 12, 2026, 2:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f77f8ffc508190a7bc69745c43a644 |
completed | May 3, 2026, 5:02 p.m. |
Created at: April 9, 2026, 9:49 p.m.