Triple
T37528821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Embassy of China in the United Kingdom |
E932977
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Press and Public Diplomacy Section of the Embassy of China in the United Kingdom
The Press and Public Diplomacy Section of the Embassy of China in the United Kingdom is the embassy’s unit responsible for media relations, public outreach, and promoting China’s image and policies to the British public.
|
E932977
|
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: Press and Public Diplomacy Section of the Embassy of China in the United Kingdom | Statement: [Embassy of China in the United Kingdom, hasPart, Press and Public Diplomacy Section of the Embassy of China in the United Kingdom]
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: Press and Public Diplomacy Section of the Embassy of China in the United Kingdom Triple: [Embassy of China in the United Kingdom, hasPart, Press and Public Diplomacy Section of the Embassy of China in the United Kingdom]
Generated description
The Press and Public Diplomacy Section of the Embassy of China in the United Kingdom is the embassy’s unit responsible for media relations, public outreach, and promoting China’s image and policies to the British public.
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_69f76ec8862c8190bfa24145f5480642 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fba3f48a2481909b6c1cbde6c2abbf |
completed | May 6, 2026, 8:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a40954618808190bce2c586f10b1295 |
completed | June 28, 2026, 3:30 a.m. |
| NEDg | Description generation | batch_6a409633e79081909e9bcabfc3b3ba0c |
completed | June 28, 2026, 3:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a4096d614b08190b70abac7855a983b |
completed | June 28, 2026, 3:36 a.m. |
Created at: May 3, 2026, 4:17 p.m.