Triple
T2823174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1978 FIFA World Cup |
E54856
|
entity |
| Predicate | attendanceTotal |
P427
|
FINISHED |
| Object | 1545791 |
—
|
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: 1545791 | Statement: [1978 FIFA World Cup, attendanceTotal, 1545791]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: attendanceTotal Context triple: [1978 FIFA World Cup, attendanceTotal, 1545791]
-
A.
attendance
Indicates the relationship between an event and the people who are present at or participate in that event.
-
B.
recordAttendance
Indicates that an entity documents or logs the presence or participation of another entity at a specific event, session, or time.
-
C.
fanAttendance
Indicates the number or presence of fans attending an event, such as a game, show, or performance.
-
D.
peakDayAttendance
Indicates the number of attendees present on the single highest-attendance day within a given period or event.
-
E.
visitorCount
chosen
Indicates the number of visitors associated with a particular entity, context, or time period.
- 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_69ab49e100c0819082a40cb797383243 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abdf15b7288190a03d1193cc0544a6 |
completed | March 7, 2026, 8:17 a.m. |
| PD | Predicate disambiguation | batch_69abdd08f2f481908c3da8a9c7a00552 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:59 p.m.