Triple
T23729335
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macau Peninsula |
E586369
|
entity |
| Predicate | connectedBy |
P37
|
FINISHED |
| Object |
Friendship Bridge
Friendship Bridge is a major roadway bridge in Macau that links the Macau Peninsula with other key parts of the territory, serving as an important transportation route.
|
E1600584
|
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: Friendship Bridge | Statement: [Macau Peninsula, connectedBy, Friendship Bridge]
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: Friendship Bridge Triple: [Macau Peninsula, connectedBy, Friendship Bridge]
Generated description
Friendship Bridge is a major roadway bridge in Macau that links the Macau Peninsula with other key parts of the territory, serving as an important transportation route.
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_69e24907dc9c8190be074c9c96a0ec2d |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b9180bf48190a6c3656ef0530463 |
completed | April 29, 2026, 7:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0f53c3dc9c8190a85075df1790d669 |
completed | May 21, 2026, 6:49 p.m. |
| NEDg | Description generation | batch_6a0f579b03d881909aa6ea3d79a030fa |
completed | May 21, 2026, 7:06 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0f581d81f88190aa2299118feb3faa |
completed | May 21, 2026, 7:08 p.m. |
Created at: April 17, 2026, 7:09 p.m.