Triple
T18642699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fed |
E455728
|
entity |
| Predicate | shortFor |
P43
|
FINISHED |
| Object | Federer |
—
|
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: Federer | Statement: [Fed, shortFor, Federer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Federer Context triple: [Fed, shortFor, Federer]
-
A.
Leo Federer
Leo Federer is one of the twin sons of Swiss tennis legend Roger Federer and his wife Miroslava (Mirka) Federer.
-
B.
Roger Federer
chosen
Roger Federer is a Swiss tennis legend widely regarded as one of the greatest players in the sport’s history, known for his record-breaking Grand Slam titles and elegant playing style.
-
C.
Thom Fischer
Thom Fischer is a person whose full name is Thomas Fischer, commonly known or referred to by this shortened form.
-
D.
Rafael Nadal
Rafael Nadal is a Spanish tennis legend widely regarded as one of the greatest players of all time, renowned for his dominance on clay courts and his numerous Grand Slam titles.
-
E.
Novak Djokovic
Novak Djokovic is a Serbian professional tennis player widely regarded as one of the greatest of all time, holding numerous Grand Slam titles and records across all major tournaments.
- 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_69d8d38ea1e88190997e9b231190ba6f |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54fcd6da081908030b052727f2c2f |
completed | April 19, 2026, 9:57 p.m. |
Created at: April 10, 2026, 11:47 a.m.