Triple
T9397608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 14 (Paris Métro) |
E226384
|
entity |
| Predicate | designedForHighCapacity |
P88719
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Line 14 (Paris Métro), designedForHighCapacity, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: designedForHighCapacity Context triple: [Line 14 (Paris Métro), designedForHighCapacity, true]
-
A.
designedCargoCapacity
Indicates the maximum amount of cargo an object (such as a vehicle or container) was originally engineered or specified to carry.
-
B.
designedToWithstand
Indicates that something has been intentionally created or engineered to resist, endure, or remain functional under specified conditions, forces, or stresses.
-
C.
typicalCapacity
Indicates the usual or standard amount, volume, or capability that something is designed or expected to hold, handle, or perform under normal conditions.
-
D.
bootCapacity
Indicates the storage volume or carrying capacity available in the boot (trunk) of a vehicle.
-
E.
capacityCategory
Indicates the classification of something based on the amount or volume it can hold, handle, or accommodate.
- 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_69ca843170f88190800a8ab2b5fc568e |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd51541020819097da2eb60be73760 |
completed | April 1, 2026, 5:09 p.m. |
| PD | Predicate disambiguation | batch_69cca545b2448190a4297312e39c21ac |
completed | April 1, 2026, 4:55 a.m. |
| PDg | Predicate description generation | batch_69cca89b3368819087a3d69270c1f185 |
completed | April 1, 2026, 5:09 a.m. |
Created at: March 30, 2026, 7:46 p.m.