Triple
T2858150
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arthur Edeson |
E63252
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Arthur |
E270757
|
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: Arthur | Statement: [Arthur Edeson, givenName, Arthur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arthur Context triple: [Arthur Edeson, givenName, Arthur]
-
A.
Arthur
Arthur is a long-running animated children's television series that follows the everyday adventures and life lessons of Arthur Read, an anthropomorphic aardvark, and his friends and family.
-
B.
Arthur
Arthur is the given name of the renowned American playwright Arthur Miller, known for works such as "Death of a Salesman" and "The Crucible."
-
C.
Arthur
Arthur is a common English-language surname borne by figures such as Chester A. Arthur, the 21st president of the United States.
-
D.
Arthur
chosen
Arthur is the given first name of the influential British computer scientist Robin Milner, known for his work in programming language theory and process calculi.
-
E.
Arthur
Arthur is a skilled point man and strategist in the film "Inception," known for his meticulous planning and execution of shared dream heists alongside Dom Cobb.
- 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_69ab4c41e8c08190a9e8f5249cc12610 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf89d3b4819086936f26d8683a2a |
completed | March 7, 2026, 8:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b08644192481909cb5ec394148adf4 |
completed | March 10, 2026, 8:59 p.m. |
Created at: March 6, 2026, 10:02 p.m.