Triple
T33723478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nadia Petrova |
E864079
|
entity |
| Predicate | olympicMedalEvent |
P7488
|
FINISHED |
| Object | women’s doubles tennis |
—
|
LITERAL 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: women’s doubles tennis | Statement: [Nadia Petrova, olympicMedalEvent, women’s doubles tennis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: olympicMedalEvent Context triple: [Nadia Petrova, olympicMedalEvent, women’s doubles tennis]
-
A.
OlympicMedalEvent
chosen
Indicates that an entity represents a specific Olympic Games event in which medals are awarded.
-
B.
olympicGoldMedalInEvent
Indicates that an entity has won an Olympic gold medal in a specified sporting event.
-
C.
medalEventsFor
Indicates a relationship where one entity lists or specifies the medal-awarding events associated with another entity.
-
D.
OlympicGoldMedalSport
Indicates that the subject sport is one in which the object athlete or team has won an Olympic gold medal.
-
E.
olympicEventType
Indicates the specific category or discipline of an event within the Olympic Games.
- F. None of above.
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_69f34989871c81908682e22a2fe4b829 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7465687bc8190a9da44d62b634ed7 |
completed | May 3, 2026, 12:57 p.m. |
| PD | Predicate disambiguation | batch_69f743f4ceb08190a21fe7f4a99b166b |
completed | May 3, 2026, 12:47 p.m. |
Created at: May 1, 2026, 1:44 a.m.