Triple
T19001461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francis X. Suarez |
E464963
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Francis X. Suarez |
—
|
NE NERFINISHED |
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: Francis X. Suarez | Statement: [Francis X. Suarez, name, Francis X. Suarez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Francis X. Suarez Context triple: [Francis X. Suarez, name, Francis X. Suarez]
-
A.
Francis X. Suarez
chosen
Francis X. Suarez is an American politician and attorney who serves as the mayor of Miami and is known for promoting the city as a hub for technology and cryptocurrency.
-
B.
Dominick J. Ruggerio
Dominick J. Ruggerio is an American Democratic politician who serves as President of the Rhode Island Senate.
-
C.
John F. Shea
John F. Shea was an American lyricist best known for writing the words to the University of Notre Dame’s iconic fight song.
-
D.
George A. Mendoza
George A. Mendoza is a film producer best known for his work on Disney’s animated feature "The Lion King 1½."
-
E.
Rafael J. Pascual
Rafael J. Pascual is a scholar of Old English language and literature who holds a prestigious professorship in Anglo-Saxon studies at the University of Oxford.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dd01a56c81909694a128c66b21d7 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d687cb2081909bf3ac761e292f22 |
completed | April 20, 2026, 7:32 a.m. |
Created at: April 10, 2026, 12:01 p.m.