Triple
T18807589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SL metro |
E459919
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object | Line 11 |
—
|
NE NERFINISHED |
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: Line 11 | Statement: [SL metro, hasLine, Line 11]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 11 Context triple: [SL metro, hasLine, Line 11]
-
A.
Line 11
chosen
Line 11 is a major Shanghai Metro route known for its long cross-city alignment connecting suburban areas with central Shanghai and serving key commercial and residential districts.
-
B.
Line 11
Line 11 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, providing high-speed urban and airport rail service across key districts.
-
C.
Line 11
Line 11 is a short, automated light metro line in the Barcelona Metro network that serves the hilly northern suburbs of the city.
-
D.
Line 10
Line 10 is a major loop line of the Beijing Subway that encircles central urban districts and serves as a key transfer route in the network.
-
E.
Line 10
Line 10 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving key residential and commercial districts.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a3d8ab9c819097834eac798ce810 |
completed | April 20, 2026, 3:56 a.m. |
Created at: April 10, 2026, 11:53 a.m.