Triple
T3161837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Batong Line |
E66118
|
entity |
| Predicate | terminus |
P388
|
FINISHED |
| Object |
Sihui
Sihui is a major Beijing Subway station in eastern Beijing that serves as a key interchange and endpoint for multiple metro lines.
|
E342209
|
NE FINISHED |
How this triple was built (4 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: Sihui | Statement: [Batong Line, terminus, Sihui]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sihui Context triple: [Batong Line, terminus, Sihui]
-
A.
Longyan
Longyan is a prefecture-level city in western Fujian Province, China, known for its Hakka culture, mountainous landscapes, and historic tulou earthen dwellings.
-
B.
Ma’anshan
Ma’anshan is an industrial city in eastern China known for its steel production and location along the lower Yangtze River.
-
C.
Jinyang
Jinyang is the historical name of the city now known as Taiyuan, a major urban and industrial center in northern China’s Shanxi province.
-
D.
Panzhihua
Panzhihua is a major industrial city in southwestern China known for its rich mineral resources, especially vanadium-titanium magnetite, and its role as a key steel-producing center.
-
E.
Yuncheng
Yuncheng is a major city in southern Shanxi Province, China, known for its historical sites and role as a regional transportation and economic hub.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Sihui Triple: [Batong Line, terminus, Sihui]
Generated description
Sihui is a major Beijing Subway station in eastern Beijing that serves as a key interchange and endpoint for multiple metro lines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sihui Target entity description: Sihui is a major Beijing Subway station in eastern Beijing that serves as a key interchange and endpoint for multiple metro lines.
-
A.
Longyan
Longyan is a prefecture-level city in western Fujian Province, China, known for its Hakka culture, mountainous landscapes, and historic tulou earthen dwellings.
-
B.
Ma’anshan
Ma’anshan is an industrial city in eastern China known for its steel production and location along the lower Yangtze River.
-
C.
Jinyang
Jinyang is the historical name of the city now known as Taiyuan, a major urban and industrial center in northern China’s Shanxi province.
-
D.
Panzhihua
Panzhihua is a major industrial city in southwestern China known for its rich mineral resources, especially vanadium-titanium magnetite, and its role as a key steel-producing center.
-
E.
Yuncheng
Yuncheng is a major city in southern Shanxi Province, China, known for its historical sites and role as a regional transportation and economic hub.
- F. None of above. chosen
Provenance (5 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_69ad85850c1481908a9e9c6242238de2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada618b9b88190afaa6d47dcad9f2c |
completed | March 8, 2026, 4:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28e24fef881908b9b9d9a67c42c0e |
completed | March 12, 2026, 9:57 a.m. |
| NEDg | Description generation | batch_69b28f6d8a948190b9aac1b90de472d6 |
completed | March 12, 2026, 10:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2c08b196881908e72596d54ab8873 |
completed | March 12, 2026, 1:32 p.m. |
Created at: March 8, 2026, 3:06 p.m.