Triple
T1110366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pantiles food and craft markets |
E25581
|
entity |
| Predicate | typicalStallType |
P24456
|
FINISHED |
| Object | food stall |
—
|
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: food stall | Statement: [Pantiles food and craft markets, typicalStallType, food stall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalStallType Context triple: [Pantiles food and craft markets, typicalStallType, food stall]
-
A.
typicalStormType
Indicates the kind of storm that is most commonly or characteristically associated with a given context or location.
-
B.
typicalSegmentType
Indicates that something is classified as belonging to a usual or characteristic type of segment within a broader structure or sequence.
-
C.
hasRunwayType
Indicates that an airport or airfield has a runway of a specified type or surface classification.
-
D.
spurType
Indicates the specific kind or category of spur associated with or used by an entity.
-
E.
landingGearType
Indicates the specific kind or configuration of landing gear that an object (typically an aircraft or vehicle) uses.
- 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_69a49428d4448190b3b36991ceae87ce |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bbd92a8c8190a16e55f3f739010f |
completed | March 1, 2026, 10:21 p.m. |
| PD | Predicate disambiguation | batch_69a4bb42990c819080db96478fd4977e |
completed | March 1, 2026, 10:18 p.m. |
| PDg | Predicate description generation | batch_69a4bbd7ff1881908c943ecdfea59e81 |
completed | March 1, 2026, 10:21 p.m. |
Created at: March 1, 2026, 7:43 p.m.