Triple
T28327597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nine-turn Bridge |
E717453
|
entity |
| Predicate | hasViewOf |
P854
|
FINISHED |
| Object |
Yuyuan Garden pavilions
The Yuyuan Garden pavilions are traditional Chinese structures within Shanghai’s historic Yuyuan Garden, noted for their classical architecture, ornamental details, and views over ponds, rockeries, and winding walkways.
|
E1813325
|
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: Yuyuan Garden pavilions | Statement: [Nine-turn Bridge, hasViewOf, Yuyuan Garden pavilions]
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: Yuyuan Garden pavilions Triple: [Nine-turn Bridge, hasViewOf, Yuyuan Garden pavilions]
Generated description
The Yuyuan Garden pavilions are traditional Chinese structures within Shanghai’s historic Yuyuan Garden, noted for their classical architecture, ornamental details, and views over ponds, rockeries, and winding walkways.
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_69eff6e9a57c8190a69c2c74b5d72119 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f6493036f4819095f94ef6457dd35d |
completed | May 2, 2026, 6:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a1627b51b288190a12ed7eeaa111d4a |
completed | May 26, 2026, 11:07 p.m. |
| NEDg | Description generation | batch_6a16295b5a4c8190bed0f47920d2bf79 |
completed | May 26, 2026, 11:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a162a080b348190923c6ee579c829c1 |
completed | May 26, 2026, 11:17 p.m. |
Created at: April 28, 2026, 12:29 a.m.