Triple
T1038875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Massey Hall |
E22424
|
entity |
| Predicate | notableRecordingVenueFor |
P24246
|
FINISHED |
| Object | jazz |
—
|
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: jazz | Statement: [Massey Hall, notableRecordingVenueFor, jazz]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableRecordingVenueFor Context triple: [Massey Hall, notableRecordingVenueFor, jazz]
-
A.
notableLocation
Indicates that a location is especially significant, prominent, or noteworthy in relation to the subject.
-
B.
notablePlace
Indicates that a place is especially significant, famous, or noteworthy in relation to the subject.
-
C.
legacyVenue
Indicates that a venue has historical or long-standing significance, often preserved or recognized due to its past importance or enduring role.
-
D.
notableCeremony
Indicates that an entity is associated with a significant or distinguished ceremony, event, or formal observance.
-
E.
notableTheater
Indicates that there is a significant or well-known theater associated with the subject.
- 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_69a493d91478819094cc01fb65564bc1 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b97c64a88190bf1119fdd4940bf3 |
completed | March 1, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69a4b729f8488190b2042bd9c625a833 |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b97acbf4819087b92a8b29baef46 |
completed | March 1, 2026, 10:11 p.m. |
Created at: March 1, 2026, 7:41 p.m.