Triple
T24893918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UEFA Women's Euro 2022 |
E623081
|
entity |
| Predicate | attendanceRecordMatchType |
P157856
|
FINISHED |
| Object | women's international |
—
|
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 international | Statement: [UEFA Women's Euro 2022, attendanceRecordMatchType, women's international]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: attendanceRecordMatchType Context triple: [UEFA Women's Euro 2022, attendanceRecordMatchType, women's international]
-
A.
attendanceRecordMatch
Indicates that two attendance records correspond to the same underlying event, person, or time entry according to defined matching criteria.
-
B.
hasAttendanceType
Indicates the specific category or mode of attendance associated with an event or participant (e.g., in-person, virtual, hybrid).
-
C.
attendanceUnit
Indicates a unit or measure used to quantify or record attendance in a given context.
-
D.
attendanceRecordVenue
Indicates that an attendance record is associated with, or took place at, a specific venue.
-
E.
matchType
Indicates the specific category or nature of how two or more entities correspond or align with each other within a given context.
- 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_69e2fac597708190a922bf39a49ec70a |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f453035f508190be83a3d521723acf |
completed | May 1, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69f44d77f6e88190a4643ab2cbef567b |
completed | May 1, 2026, 6:51 a.m. |
| PDg | Predicate description generation | batch_69f45300bd488190bb1d4160f5534ef6 |
completed | May 1, 2026, 7:15 a.m. |
Created at: April 18, 2026, 5:26 a.m.