Triple
T13140050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shijiazhuang Metro |
E312187
|
entity |
| Predicate | numberOfLinesInOperation |
P4876
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Shijiazhuang Metro, numberOfLinesInOperation, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLinesInOperation Context triple: [Shijiazhuang Metro, numberOfLinesInOperation, 3]
-
A.
hasNumberOfLines
chosen
Indicates the relationship that specifies how many lines are associated with a given entity.
-
B.
numberOperational
Indicates that an entity is currently functioning and available for use in its intended operational capacity.
-
C.
fastLinesUsedBy
Indicates that certain fast transportation or communication lines are utilized by a particular entity.
-
D.
numberOfExecutions
Indicates the count of times a particular action, process, or event has been carried out.
-
E.
numberOfNewInstructions
Indicates the count of newly added or introduced instructions associated with an entity or process.
- 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_69d806aabde48190899e13e41659cae5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d981b84f1081908b9e2d54a64d4c2d |
completed | April 10, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69d9804543cc8190a23cd7da59a12a7b |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:09 p.m.