Triple
T10263850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Diego Convention Center |
E240666
|
entity |
| Predicate | hasConventionHalls |
P93010
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [San Diego Convention Center, hasConventionHalls, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasConventionHalls Context triple: [San Diego Convention Center, hasConventionHalls, yes]
-
A.
hasBallrooms
Indicates that an entity possesses or contains one or more ballrooms as part of its facilities or structure.
-
B.
numberOfHalls
Indicates the quantity of halls associated with a given entity or location.
-
C.
hasSingleHall
Indicates that an entity possesses exactly one hall within its structure or domain.
-
D.
hasAuditorium
Indicates that one entity possesses or includes an auditorium as part of its facilities.
-
E.
hasTicketHall
Indicates that a place or facility includes or is equipped with a designated ticket hall area for purchasing or validating tickets.
- 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_69d381a94c1881908fc38fc263d9b9c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2872830819080fdfa816167d04c |
completed | April 7, 2026, 9:46 a.m. |
| PD | Predicate disambiguation | batch_69d4d1ef6e6c81908a8ee52e4d28127b |
completed | April 7, 2026, 9:44 a.m. |
| PDg | Predicate description generation | batch_69d4d285812c8190ab910dc85c53eebf |
completed | April 7, 2026, 9:46 a.m. |
Created at: April 6, 2026, 11:33 a.m.