Triple
T3765945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TGV POS |
E82676
|
entity |
| Predicate | numberOfTrainsetsBuilt |
P50983
|
FINISHED |
| Object | 19 |
—
|
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: 19 | Statement: [TGV POS, numberOfTrainsetsBuilt, 19]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTrainsetsBuilt Context triple: [TGV POS, numberOfTrainsetsBuilt, 19]
-
A.
vehiclesPerTrain
Indicates the number of vehicles that are attached to or make up a single train.
-
B.
introducedRollingStock
Indicates that an entity caused new rolling stock (such as trains or rail vehicles) to be put into service or use.
-
C.
formerRollingStock
Indicates that an entity was previously used as rolling stock (e.g., railway vehicles) but no longer serves in that capacity.
-
D.
ownedRollingStock
Indicates that one entity possesses or has ownership rights over specific rolling stock (such as trains, railcars, or locomotives).
-
E.
railcode
Indicates that an entity is associated with a specific railway code used for identification or classification within a rail system.
- 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_69ad8b207b0081909d2b48843fbd8795 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbfeb52081909c38103beb5dbdcd |
completed | March 8, 2026, 7:20 p.m. |
| PD | Predicate disambiguation | batch_69adc04ec36c8190bd5b944d4f4d32aa |
completed | March 8, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69adc133ef50819094c2b971f31f1615 |
completed | March 8, 2026, 6:34 p.m. |
Created at: March 8, 2026, 3:35 p.m.