Triple
T29796754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jiaotong University station |
E756572
|
entity |
| Predicate | hasAdjacentStationOnLine 10 |
P119311
|
FINISHED |
| Object | Shanghai Library station |
—
|
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: Shanghai Library station | Statement: [Jiaotong University station, hasAdjacentStationOnLine 10, Shanghai Library station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAdjacentStationOnLine 10 Context triple: [Jiaotong University station, hasAdjacentStationOnLine 10, Shanghai Library station]
-
A.
hasAdjacentStationOnLine 2
Indicates that one station is directly next to another station along Line 2 in the network.
-
B.
hasAdjacentStationOnLine 5
Indicates that one station is directly next to another station along line 5, with no other stations in between on that line.
-
C.
adjacentStationOnLine10
chosen
Indicates that two stations are directly next to each other along transit line 10, with no other station between them on that line.
-
D.
hasAdjacentStationOnLine1
Indicates that one station is directly next to another station along Line 1 in the network.
-
E.
hasAdjacentStationOnLine 7bis
Indicates that one station is directly next to another station along metro line 7bis.
- 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_69f22454583081908927516cb9938d1d |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f7886be6d8819095ec62e4f2cee858 |
completed | May 3, 2026, 5:39 p.m. |
| PD | Predicate disambiguation | batch_69f7841440f48190b4346c08855951d2 |
completed | May 3, 2026, 5:21 p.m. |
Created at: April 29, 2026, 5:15 p.m.