Triple
T994088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Secretary |
E21455
|
entity |
| Predicate | relatedForm |
P21674
|
FINISHED |
| Object | Mr. President |
E1156
|
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: Mr. President | Statement: [Mr. Secretary, relatedForm, Mr. President]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mr. President Context triple: [Mr. Secretary, relatedForm, Mr. President]
-
A.
Mr. President
chosen
"Mr. President" is the formal spoken address traditionally used for the sitting President of the United States.
-
B.
Mr. President
"Mr. President" is the formal style of address used for the presiding officer of the Massachusetts Senate.
-
C.
Mr. President
"Mr. President" is a formal style of address used for the President of Ecuador.
-
D.
Mr. Vice President
Mr. Vice President is the formal spoken and written title used to address the sitting Vice President of the United States.
-
E.
Bon Voyage, Mr. President
"Bon Voyage, Mr. President" is a short story by Gabriel García Márquez that follows an exiled Caribbean dictator facing illness, nostalgia, and political ghosts while living in Geneva.
- 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_69a493c476b48190b41fc5e793171cc6 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b7570b388190ada9693935792a58 |
completed | March 1, 2026, 10:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac25919bcc8190886f19405536681b |
completed | March 7, 2026, 1:18 p.m. |
Created at: March 1, 2026, 7:41 p.m.