Triple
T16485570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caishikou station |
E400429
|
entity |
| Predicate | line |
P1293
|
FINISHED |
| Object | Line 4 |
E451278
|
NE 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: Line 4 | Statement: [Caishikou station, line, Line 4]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 4 Context triple: [Caishikou station, line, Line 4]
-
A.
Line 4
Line 4 is a rapid transit route of the STC Metro system, serving as one of its main urban rail lines.
-
B.
Line 4
Line 4 is a proposed circular metro line intended to complete and expand the Seville Metro network in Seville, Spain.
-
C.
Line 4
Line 4 is one of the main north–south lines of the Paris Métro, known for serving central Paris and connecting key railway stations and neighborhoods.
-
D.
Line 4
Line 4 is a planned rapid transit route within the future Ho Chi Minh City Metro system in Vietnam.
-
E.
Line 4
chosen
Line 4 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving as one of the city's major north–south corridors.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d883813098819084f5409539723b59 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e068fac8190a0971f548848fcfe |
completed | April 18, 2026, 7:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00582275308190a0fb3944d74916cf |
completed | May 10, 2026, 10:04 a.m. |
Created at: April 10, 2026, 5:13 a.m.