Triple
T4197889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thomas Sharpe |
E85996
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Sharpe |
E136827
|
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: Sharpe | Statement: [Thomas Sharpe, familyName, Sharpe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sharpe Context triple: [Thomas Sharpe, familyName, Sharpe]
-
A.
Sharpe
chosen
Sharpe is the surname of Shannon Sharpe, a Hall of Fame former NFL tight end and prominent sports analyst.
-
B.
Sharpe
Sharpe is a British television drama series based on Bernard Cornwell’s novels, following the adventures of soldier Richard Sharpe during the Napoleonic Wars.
-
C.
Richard Sharpe
Richard Sharpe is the fictional British soldier and officer from Bernard Cornwell’s historical novels, best known through the television adaptations in which he rises through the ranks during the Napoleonic Wars.
-
D.
Richard Bowdler Sharpe
Richard Bowdler Sharpe was a 19th-century English zoologist and ornithologist known for his extensive work on bird classification and descriptions, including numerous species of raptors.
-
E.
Outram
Outram is a central district in Singapore known for its major medical facilities, heritage architecture, and proximity to the downtown core.
- 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_69aed93b89f48190a31f6d57c760e42f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af0360bc8081908ceb2483eef89174 |
completed | March 9, 2026, 5:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b58a0ff2a08190ac7f89d306454ab8 |
completed | March 14, 2026, 4:17 p.m. |
Created at: March 9, 2026, 3:48 p.m.