Triple
T3461565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jianguomen station |
E73036
|
entity |
| Predicate | hasStationCodeLine1 |
P1289
|
FINISHED |
| Object | 120 |
—
|
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: 120 | Statement: [Jianguomen station, hasStationCodeLine1, 120]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStationCodeLine1 Context triple: [Jianguomen station, hasStationCodeLine1, 120]
-
A.
hasStationCode
chosen
Indicates that an entity is associated with a specific station identification code.
-
B.
usesLineCode
Indicates that one entity employs or references a specific line code as part of its operation, identification, or communication.
-
C.
hasAdjacentStationOnLine12
Indicates that one station is directly next to another station along transit line 12, with no other stations in between on that line.
-
D.
hasAdjacentStationOnLine1
Indicates that one station is directly next to another station along Line 1 in the network.
-
E.
hasRailwayLineNumber
Indicates the specific identification number assigned to a railway line associated with an entity.
- 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_69ad85b224d481908ff8be51338d24ff |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbae745e081909007cd3a664c57f3 |
completed | March 8, 2026, 6:07 p.m. |
| PD | Predicate disambiguation | batch_69adae05bb0081909dc7e4779d6e05ef |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:17 p.m.