Triple
T3097258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roy Obama |
E64626
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Roy |
E54928
|
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: Roy | Statement: [Roy Obama, givenName, Roy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roy Context triple: [Roy Obama, givenName, Roy]
-
A.
Roy
chosen
Roy is a common masculine given name of Celtic origin, often used independently or as a nickname for longer names.
-
B.
Robert
Robert is a common masculine given name of Germanic origin, widely used in English-speaking countries.
-
C.
George
George is the first name of George Washington, the first President of the United States and a key leader in the American Revolutionary War.
-
D.
George
George is a town in South Africa’s Western Cape province, known as a gateway to the Garden Route and for its scenic mountains and forests.
-
E.
George
George is the heroic protagonist of the fantasy film "The Magic Sword," known for embarking on a perilous quest to rescue a princess from an evil sorcerer.
- 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_69ad857dc98481909e585dc3372e3ed5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada23cbe3c8190b7ec5cfd464a1ca8 |
completed | March 8, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b224bff19c8190b28c07e3fb018853 |
completed | March 12, 2026, 2:28 a.m. |
Created at: March 8, 2026, 3:03 p.m.