Triple
T6165865
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicago, Burlington & Quincy Railroad Co. v. Chicago |
E137555
|
entity |
| Predicate | volumeInUSReports |
P3130
|
FINISHED |
| Object | 166 |
—
|
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: 166 | Statement: [Chicago, Burlington & Quincy Railroad Co. v. Chicago, volumeInUSReports, 166]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: volumeInUSReports Context triple: [Chicago, Burlington & Quincy Railroad Co. v. Chicago, volumeInUSReports, 166]
-
A.
volumeInUnitedStatesReports
chosen
Indicates that a legal case or decision is published in a specified volume of the United States Reports.
-
B.
volume
Indicates the amount of three-dimensional space an entity occupies or contains.
-
C.
foundInVolume
Indicates that one entity is physically or logically contained within, or occurs in, a specific volume (such as a book volume, data volume, or bounded collection).
-
D.
meanVolume_km3
Indicates the average volume of an entity, measured in cubic kilometers (km³), typically over a specified period or set of conditions.
-
E.
numberOfVolumes
Indicates the total count of separate volumes or parts that make up a multi-volume work or collection.
- 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_69c008a54fc88190b6ce4416490ca79d |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05d6225bc819097707be620681e7b |
completed | March 22, 2026, 9:21 p.m. |
| PD | Predicate disambiguation | batch_69c055f5b81481908819515cdc334ae6 |
completed | March 22, 2026, 8:49 p.m. |
Created at: March 22, 2026, 4:17 p.m.