Triple
T14099457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jiulongshan |
E339341
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object | Line 14: 14-05 |
unclear NED1
|
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 14: 14-05 | Statement: [Jiulongshan, hasStationCode, Line 14: 14-05]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 14: 14-05 Context triple: [Jiulongshan, hasStationCode, Line 14: 14-05]
-
A.
Line 14
Line 14 is a major rapid transit line of the Beijing Subway system that serves multiple key residential and commercial districts across the city.
-
B.
Line 14
Line 14 is a rapid transit line of the Guangzhou Metro system in Guangzhou, China, serving suburban and outlying districts with high-speed, longer-distance urban rail service.
-
C.
Line 14
Line 14 is a rapid transit line of the Shanghai Metro system that serves as one of the city's major east–west corridors.
-
D.
Line 14
Line 14 is a fully automated, high-capacity line of the Paris Métro known for its modern trains and role in relieving congestion on central routes.
-
E.
Line 14
Line 14 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving as part of the city's expanding urban rail network.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d81c69b5c8819094aa1abf18302908 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5fba7c10819095b1299b7b4f0310 |
completed | April 14, 2026, 3:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcd0adfc28819097a1bfd56739c286 |
completed | May 7, 2026, 5:49 p.m. |
Created at: April 9, 2026, 10:22 p.m.