Triple
T3847688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Phelps won eight gold medals in swimming |
E85211
|
entity |
| Predicate | hasNumberOfEventsWon |
P27026
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Michael Phelps won eight gold medals in swimming, hasNumberOfEventsWon, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfEventsWon Context triple: [Michael Phelps won eight gold medals in swimming, hasNumberOfEventsWon, 8]
-
A.
numberOfWins
chosen
Indicates the count of times an entity has achieved victory in a relevant context or competition.
-
B.
serviceNumberOfVictories
Indicates the number of victories achieved in the context of a particular service or service-related activity.
-
C.
gamesWonBy
Indicates the number of games that have been won by a particular entity in a given context.
-
D.
careerWins
Indicates the total number of wins an individual or entity has accumulated over the course of their entire career.
-
E.
winnerCount
Indicates the number of entities that are designated as winners in a given context or event.
- 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_69aed936de1c81908f91bed80f70abb2 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeebcc8a0481909c35161336bdfbf9 |
completed | March 9, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69aee750377c8190af70c79768c0edd8 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:18 p.m.