Triple
T8675357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Tree |
E205898
|
entity |
| Predicate | notableStudent |
P4838
|
FINISHED |
| Object | Roberto Díaz |
E208443
|
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: Roberto Díaz | Statement: [Michael Tree, notableStudent, Roberto Díaz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roberto Díaz Context triple: [Michael Tree, notableStudent, Roberto Díaz]
-
A.
Roberto Díaz
chosen
Roberto Díaz is a renowned violist and music educator who has served as president and CEO of the Curtis Institute of Music.
-
B.
Diego Valeri
Diego Valeri is an Argentine attacking midfielder best known as a star playmaker and club icon for Major League Soccer’s Portland Timbers.
-
C.
Sergio González
Sergio González is an alumnus of Gulliver Preparatory School, a private college-preparatory institution in Miami, Florida.
-
D.
Raúl Ruidíaz
Raúl Ruidíaz is a Peruvian professional footballer and prolific striker known for his scoring exploits in Major League Soccer with the Seattle Sounders and for the Peru national team.
-
E.
Roberto Henríquez
Roberto Henríquez is an editor known for his work on the film "Brave."
- 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_69ca83529a9c8190b5c075b4f14636ed |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc49f54dfc8190b7a61e7ed1cfcbeb |
completed | March 31, 2026, 10:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cef3960ce881908f07fb9fdafcd550 |
completed | April 2, 2026, 10:54 p.m. |
Created at: March 30, 2026, 6:32 p.m.