Triple
T19196088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 48 (Shenzhen Metro) |
E469970
|
entity |
| Predicate | hasPlannedRollingStockType |
P134828
|
FINISHED |
| Object | metro trains |
—
|
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: metro trains | Statement: [Line 48 (Shenzhen Metro), hasPlannedRollingStockType, metro trains]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPlannedRollingStockType Context triple: [Line 48 (Shenzhen Metro), hasPlannedRollingStockType, metro trains]
-
A.
usesRollingStock
Indicates that one entity employs or operates specific rolling stock (such as rail vehicles) in its activities or services.
-
B.
hasRollingStockFeature
Indicates that a piece of rolling stock possesses a specific feature, characteristic, or equipment.
-
C.
usesRollingStockCompatibleWith
Indicates that one entity operates using rolling stock that is technically and operationally compatible with the rolling stock standards or systems associated with another entity.
-
D.
passengerRollingStock
Indicates that the rolling stock is designed or used for carrying passengers rather than freight or other purposes.
-
E.
ownedRollingStock
Indicates that one entity possesses or has ownership rights over specific rolling stock (such as trains, railcars, or locomotives).
- 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_69d8dd0ad9088190a173b32657ae2e7a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f8a5dabc81908fffad811f177b03 |
completed | April 20, 2026, 9:57 a.m. |
| PD | Predicate disambiguation | batch_69e4b9bb158481909478ca2e06f3ba39 |
completed | April 19, 2026, 11:17 a.m. |
| PDg | Predicate description generation | batch_69e4bfe9ef7081908a74a57d1fc731ea |
completed | April 19, 2026, 11:43 a.m. |
Created at: April 10, 2026, 12:07 p.m.