Triple
T31258830
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Paul metro station |
E797057
|
entity |
| Predicate | lineAutomation |
P171511
|
FINISHED |
| Object | Line 1 automated operation |
—
|
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: Line 1 automated operation | Statement: [Saint-Paul metro station, lineAutomation, Line 1 automated operation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lineAutomation Context triple: [Saint-Paul metro station, lineAutomation, Line 1 automated operation]
-
A.
lineConfiguration
Indicates that the entities participate together in a specific arrangement or pattern along a line.
-
B.
lineUse
Indicates how a particular line (such as a route, track, or service line) is utilized or purposed within a system or network.
-
C.
networkLine
Indicates that there is a connection or linkage between entities within a network structure or system.
-
D.
channelLining
Indicates the presence or application of a protective or functional lining along the interior surface of a channel or conduit.
-
E.
lineUses
Indicates that a particular line (such as a route, service, or connection) makes use of or is implemented using a specified resource, infrastructure, or element.
- 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_69f224dd5fdc81908a4cd24917b67668 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f69f80b62c8190bf2af2be0d3a7df8 |
completed | May 3, 2026, 1:06 a.m. |
| PD | Predicate disambiguation | batch_69f69d1bf8cc8190a78dfa5ab00daf3a |
completed | May 3, 2026, 12:55 a.m. |
| PDg | Predicate description generation | batch_69f69edae2448190925ce701c8792c52 |
completed | May 3, 2026, 1:03 a.m. |
Created at: April 29, 2026, 9:12 p.m.