Triple
T1202562
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GM Motorama |
E25815
|
entity |
| Predicate | typicalVenues |
P25526
|
FINISHED |
| Object | convention centers |
—
|
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: convention centers | Statement: [GM Motorama, typicalVenues, convention centers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalVenues Context triple: [GM Motorama, typicalVenues, convention centers]
-
A.
primaryVenues
Indicates the main or most important venues associated with or used by a given entity.
-
B.
typicalVenueCity
Indicates that a particular city is the usual or standard location where an event, activity, or organization is typically held or based.
-
C.
primaryVenueFor
Indicates that one entity serves as the main or principal venue or location for events, activities, or operations associated with another entity.
-
D.
usualVenueSince
Indicates that a particular venue has been the regular or customary location for something (e.g., an event or activity) starting from a specified point in time.
-
E.
venueConcept
Indicates a relationship where a venue is associated with, characterized by, or defined in terms of a particular concept or thematic idea.
- 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_69a4942b30f08190a91c60573e16b5ef |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bdbda0b081909c0147121a945e27 |
completed | March 1, 2026, 10:29 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5ed2b88190aab992913957e1cf |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bcc82e38819081c3615e1cc7a66f |
completed | March 1, 2026, 10:25 p.m. |
Created at: March 1, 2026, 7:46 p.m.