Triple
T14916832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nanking Road |
E371402
|
entity |
| Predicate | hasMajorIntersection |
P12328
|
FINISHED |
| Object |
Shanxi Road
Shanxi Road is a major thoroughfare in central Shanghai, China, known for intersecting key commercial streets and serving as an important urban traffic artery.
|
E1131083
|
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: Shanxi Road | Statement: [Nanking Road, hasMajorIntersection, Shanxi Road]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shanxi Road Context triple: [Nanking Road, hasMajorIntersection, Shanxi Road]
-
A.
Shenxiang Road
Shenxiang Road is a station on Shanghai's Metro network serving Line 17 in the city's western suburbs.
-
B.
Láng Road
Láng Road is a major urban thoroughfare in Hanoi, Vietnam, running along the Tô Lịch River and serving as a key traffic route in the city.
-
C.
Yongkang Road
Yongkang Road is a popular street in Shanghai known for its lively bars, cafés, and expat-friendly nightlife within the former French Concession area.
-
D.
Yishan Road
Yishan Road is a Shanghai Metro interchange station serving multiple lines in the Xuhui District of Shanghai, China.
-
E.
Hailun Road
Hailun Road is a Shanghai Metro interchange station located in Hongkou District, serving as a stop on multiple urban rail lines.
- 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: Shanxi Road Triple: [Nanking Road, hasMajorIntersection, Shanxi Road]
Generated description
Shanxi Road is a major thoroughfare in central Shanghai, China, known for intersecting key commercial streets and serving as an important urban traffic artery.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shanxi Road Target entity description: Shanxi Road is a major thoroughfare in central Shanghai, China, known for intersecting key commercial streets and serving as an important urban traffic artery.
-
A.
Shenxiang Road
Shenxiang Road is a station on Shanghai's Metro network serving Line 17 in the city's western suburbs.
-
B.
Láng Road
Láng Road is a major urban thoroughfare in Hanoi, Vietnam, running along the Tô Lịch River and serving as a key traffic route in the city.
-
C.
Yongkang Road
Yongkang Road is a popular street in Shanghai known for its lively bars, cafés, and expat-friendly nightlife within the former French Concession area.
-
D.
Yishan Road
Yishan Road is a Shanghai Metro interchange station serving multiple lines in the Xuhui District of Shanghai, China.
-
E.
Hailun Road
Hailun Road is a Shanghai Metro interchange station located in Hongkou District, serving as a stop on multiple urban rail lines.
- 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_69d85cc7ea3481908228b5acb7d06f12 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded62038508190946499cd3552990e |
completed | April 15, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe968bbbac8190a258c42b226f9def |
completed | May 9, 2026, 2:06 a.m. |
| NEDg | Description generation | batch_69fe97dd344881908619516475c359d8 |
completed | May 9, 2026, 2:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe9894bcec8190a764040910181020 |
completed | May 9, 2026, 2:14 a.m. |
Created at: April 10, 2026, 2:32 a.m.