Triple
T11287388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Métro Line 13 |
E267231
|
entity |
| Predicate | hasNotableCongestionIssues |
P54842
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Paris Métro Line 13, hasNotableCongestionIssues, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableCongestionIssues Context triple: [Paris Métro Line 13, hasNotableCongestionIssues, yes]
-
A.
hasHeavyTraffic
chosen
Indicates that a location, route, or area is experiencing a high volume of traffic, causing congestion or delays.
-
B.
hasCommuterTraffic
Indicates that there is regular, recurring traffic flow associated with people traveling between their homes and places of work or study.
-
C.
hasPassengerCongestionControls
Indicates that an entity includes mechanisms or measures to manage, limit, or alleviate congestion caused by passengers.
-
D.
hasTransportIssue
Indicates that an entity is experiencing a problem, disruption, or malfunction related to transportation or a transport service.
-
E.
hasTruckTraffic
Indicates that there is truck-related vehicular movement or flow occurring on or through a specified location or route.
- 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_69d6aac993a08190a6f36445ebaf9a43 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e986b0f08190a414749eaa7f1a5d |
completed | April 9, 2026, 6:01 p.m. |
| PD | Predicate disambiguation | batch_69d787a240588190aa097298f951c915 |
completed | April 9, 2026, 11:04 a.m. |
Created at: April 8, 2026, 9:32 p.m.