Triple
T4378249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hampden Park |
E99060
|
entity |
| Predicate | recordAttendanceYear |
P56475
|
FINISHED |
| Object | 1937 |
—
|
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: 1937 | Statement: [Hampden Park, recordAttendanceYear, 1937]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recordAttendanceYear Context triple: [Hampden Park, recordAttendanceYear, 1937]
-
A.
recordAttendance
Indicates that an entity documents or logs the presence or participation of another entity at a specific event, session, or time.
-
B.
ordinationYear
Indicates the year in which an individual was formally ordained to a religious office or role.
-
C.
recordAttendanceCompetition
Indicates that an entity records or logs attendance information for a competition or competitive event.
-
D.
scheduledInYear
Indicates that an event, activity, or item is planned or set to occur within a specific calendar year.
-
E.
authorizationYear
Indicates the year in which an official approval, permission, or authorization for something was granted.
- 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_69b3454ea8f48190a49c2436624d6ef6 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35240092c81908e26ff607d665e7a |
completed | March 12, 2026, 11:54 p.m. |
| PD | Predicate disambiguation | batch_69b34f557fe8819085032bf7f0cea5dc |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b35034cd248190bae09e9d090e13ec |
completed | March 12, 2026, 11:45 p.m. |
Created at: March 12, 2026, 11:18 p.m.