Triple
T19854078
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicago Transit Authority Brown Line stations |
E477084
|
entity |
| Predicate | haveStructureType |
P841
|
FINISHED |
| Object | elevated |
—
|
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: elevated | Statement: [Chicago Transit Authority Brown Line stations, haveStructureType, elevated]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveStructureType Context triple: [Chicago Transit Authority Brown Line stations, haveStructureType, elevated]
-
A.
hasStructureType
chosen
Indicates that an entity possesses or is classified by a specific structural type or configuration.
-
B.
haveStructure
Indicates that an entity possesses a particular internal organization, arrangement, or structural composition.
-
C.
hasStructureCode
Indicates that an entity is associated with a specific structural identifier or code that defines its organization or configuration.
-
D.
designedStructureType
Indicates the type or category of structure that something has been specifically designed to be.
-
E.
hasRealStructure
Indicates that an abstract object or system is endowed with a concrete, well-defined mathematical or physical structure that makes it realizable or interpretable in a real-world or formal model.
- 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6586aa1dc8190b6cfe051a57e338b |
completed | April 20, 2026, 4:46 p.m. |
| PD | Predicate disambiguation | batch_69e537e21d2881909b1be82f02b99d40 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:51 p.m.