Triple
T4008094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haidian District |
E89575
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Wudaokou
Wudaokou is a bustling neighborhood in Beijing known for its universities, tech companies, and vibrant student nightlife.
|
E410697
|
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: Wudaokou | Statement: [Haidian District, contains, Wudaokou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wudaokou Context triple: [Haidian District, contains, Wudaokou]
-
A.
Lüshunkou
Lüshunkou is a strategically important port city at the tip of the Liaodong Peninsula in northeastern China, historically known as Port Arthur and the site of major naval and military conflicts.
-
B.
Caishikou
Caishikou is a subway station in central Beijing that serves as an important stop on the city’s urban rail network.
-
C.
Jinqiao
Jinqiao is a subdistrict in Shanghai’s Pudong New Area known for its residential communities and growing commercial and industrial zones.
-
D.
Lujiazui
Lujiazui is Shanghai’s major financial district and futuristic skyline area on the east bank of the Huangpu River, known for its cluster of iconic skyscrapers.
-
E.
Zhushikou
Zhushikou is a subway station on the Beijing Subway system serving the central area near Beijing’s historic old city.
- 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: Wudaokou Triple: [Haidian District, contains, Wudaokou]
Generated description
Wudaokou is a bustling neighborhood in Beijing known for its universities, tech companies, and vibrant student nightlife.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wudaokou Target entity description: Wudaokou is a bustling neighborhood in Beijing known for its universities, tech companies, and vibrant student nightlife.
-
A.
Lüshunkou
Lüshunkou is a strategically important port city at the tip of the Liaodong Peninsula in northeastern China, historically known as Port Arthur and the site of major naval and military conflicts.
-
B.
Caishikou
Caishikou is a subway station in central Beijing that serves as an important stop on the city’s urban rail network.
-
C.
Jinqiao
Jinqiao is a subdistrict in Shanghai’s Pudong New Area known for its residential communities and growing commercial and industrial zones.
-
D.
Lujiazui
Lujiazui is Shanghai’s major financial district and futuristic skyline area on the east bank of the Huangpu River, known for its cluster of iconic skyscrapers.
-
E.
Zhushikou
Zhushikou is a subway station on the Beijing Subway system serving the central area near Beijing’s historic old city.
- 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_69aed9585e788190bec2d39deba3750f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefa647f80819081180eb267f1cfcc |
completed | March 9, 2026, 4:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5629004208190964cb4d2e75b0a05 |
completed | March 14, 2026, 1:28 p.m. |
| NEDg | Description generation | batch_69b5637cd5b881909a930ecb33eed991 |
completed | March 14, 2026, 1:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b564603b4881909e80970aa21e3db4 |
completed | March 14, 2026, 1:36 p.m. |
Created at: March 9, 2026, 3:34 p.m.