Triple
T36719470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SS Empress of Britain (1982) |
E907009
|
entity |
| Predicate | hasFormerName |
P65
|
FINISHED |
| Object |
SS Royal Majesty
SS Royal Majesty was a mid-sized cruise ship built in 1982 that operated for various cruise lines under multiple names, including SS Empress of Britain.
|
E2195347
|
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: SS Royal Majesty | Statement: [SS Empress of Britain (1982), hasFormerName, SS Royal Majesty]
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: SS Royal Majesty Triple: [SS Empress of Britain (1982), hasFormerName, SS Royal Majesty]
Generated description
SS Royal Majesty was a mid-sized cruise ship built in 1982 that operated for various cruise lines under multiple names, including SS Empress of Britain.
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_69f76e73ad108190a5241585f2303e9a |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f7c84319dc8190987c08469720d6b1 |
completed | May 3, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3a38376c188190871e7fb00dbe8a64 |
completed | June 23, 2026, 7:39 a.m. |
| NEDg | Description generation | batch_6a3a38cef65c8190a4c5dafe793fcf7c |
completed | June 23, 2026, 7:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a3a3a51dabc81909cf57f44ec196576 |
completed | June 23, 2026, 7:48 a.m. |
Created at: May 3, 2026, 4:12 p.m.