Triple
T25904162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UEFA Women’s Euro 2022 Golden Boot |
E652704
|
entity |
| Predicate | assistsByWinner |
P159553
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [UEFA Women’s Euro 2022 Golden Boot, assistsByWinner, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: assistsByWinner Context triple: [UEFA Women’s Euro 2022 Golden Boot, assistsByWinner, 5]
-
A.
winnerAssistsPerGameApprox
Indicates the approximate number of assists per game recorded by the winning participant or team.
-
B.
assistsOver
Indicates that one entity provides help or support to another entity in a way that surpasses or exceeds a certain reference level, standard, or counterpart.
-
C.
scoredAssists
Indicates that one entity contributed an assist that led to another entity scoring (typically in a game or sports context).
-
D.
axisOpponentsAssisted
Indicates that the subject entity provided assistance or support to opponents of the Axis powers.
-
E.
topAssistsLeader
Indicates that the subject is the leading provider of assists, typically having the highest number of assists in a given group, league, or time period.
- F. None of above. chosen
Provenance (4 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_69e7ab3d3f8481909bc53ed64c06af33 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f603bd60c08190b6032dff3bdaf5e3 |
completed | May 2, 2026, 2:01 p.m. |
| PD | Predicate disambiguation | batch_69f4a0fed15881909b789251fe5d8d45 |
completed | May 1, 2026, 12:47 p.m. |
| PDg | Predicate description generation | batch_69f55e497fa081909bc59a7b92c5df59 |
completed | May 2, 2026, 2:15 a.m. |
Created at: April 22, 2026, 8:26 a.m.