Triple
T1620193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Union Station (St. Louis) |
E35011
|
entity |
| Predicate | hasLightShow |
P31011
|
FINISHED |
| Object | Grand Hall 3D light show |
—
|
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: Grand Hall 3D light show | Statement: [Union Station (St. Louis), hasLightShow, Grand Hall 3D light show]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLightShow Context triple: [Union Station (St. Louis), hasLightShow, Grand Hall 3D light show]
-
A.
hasLaserShow
Indicates that an entity features or offers a laser-based visual show as part of its activities or attractions.
-
B.
hasLighting
Indicates that one entity is equipped with, contains, or is characterized by a particular type or configuration of lighting.
-
C.
hasRunwayLighting
Indicates that a runway is equipped with lighting systems to aid visibility and operations, typically during low-light or night conditions.
-
D.
hasNumberOfMainLights
Indicates the relationship that specifies how many primary or main lights are associated with an entity.
-
E.
hasColorPlay
Indicates a relationship where something exhibits or incorporates playful or varied use of color.
- 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_69a886023194819080a3fccd6e325d0e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf4a0ef748190ae52b9656474c0ef |
completed | March 6, 2026, 3:37 p.m. |
| PD | Predicate disambiguation | batch_69a907c731808190a1d998155041b3c1 |
completed | March 5, 2026, 4:34 a.m. |
| PDg | Predicate description generation | batch_69a99ca48c888190876500df1a885c11 |
completed | March 5, 2026, 3:09 p.m. |
Created at: March 4, 2026, 7:28 p.m.