Triple
T10460516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Burns |
E246659
|
entity |
| Predicate | hasBodyguard |
P957
|
FINISHED |
| Object | Wayne |
E49609
|
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: Wayne | Statement: [Mr. Burns, hasBodyguard, Wayne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wayne Context triple: [Mr. Burns, hasBodyguard, Wayne]
-
A.
Wayne
chosen
Wayne is a masculine given name of English origin commonly used in the United States and other English-speaking countries.
-
B.
Wayne
Wayne is a suburban township in Passaic County, New Jersey, known for its residential communities, shopping centers, and proximity to New York City.
-
C.
Wayne
Wayne is a suburban community in Pennsylvania’s Main Line region, known for its residential neighborhoods and commuter access to Philadelphia.
-
D.
Vaughn
Vaughn is a surname most prominently associated with English film director and producer Matthew Vaughn, known for stylish action and comic-book adaptations.
-
E.
Willis
Willis is a masculine given name and surname of English origin, often considered a variant or cognate of the name Wilson.
- 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_69d381c04fe08190957c26c526a3b05a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d50882eb0c8190a4311634b867eab1 |
completed | April 7, 2026, 1:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d89fcc84b48190a39de0d9b9111ebd |
completed | April 10, 2026, 6:59 a.m. |
Created at: April 6, 2026, 12:18 p.m.