Triple
T36927064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Petaluma Antique Faire |
E913373
|
entity |
| Predicate | typicalNumberOfVendors |
P5785
|
FINISHED |
| Object | over 180 |
—
|
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: over 180 | Statement: [Petaluma Antique Faire, typicalNumberOfVendors, over 180]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalNumberOfVendors Context triple: [Petaluma Antique Faire, typicalNumberOfVendors, over 180]
-
A.
hasApproximateVendorCount
chosen
Indicates that an entity is associated with an estimated or non-exact number of vendors.
-
B.
eligibleVendors
Indicates that certain vendors meet the required criteria or conditions to be considered eligible for a specified purpose or process.
-
C.
typicalNumberOfComponents
Indicates the usual or standard count of distinct components that an entity is expected to have.
-
D.
slotCountTypical
Indicates the usual or standard number of slots associated with an entity under normal conditions.
-
E.
numberOfVenues
Indicates the total count of venues associated with a given entity or context.
- 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_69f76e896c988190880c130e01303dd4 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a10036481909c71188b2a0e7f04 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:13 p.m.