Triple
T36650183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SS Empress of Britain (1980) |
E904826
|
entity |
| Predicate | renamed |
P1742
|
FINISHED |
| Object |
Queen Anna Maria
Queen Anna Maria is a cruise ship, originally launched as SS Empress of Britain in 1955, that later sailed under the Greek Line after being renamed.
|
E2193015
|
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: Queen Anna Maria | Statement: [SS Empress of Britain (1980), renamed, Queen Anna Maria]
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: Queen Anna Maria Triple: [SS Empress of Britain (1980), renamed, Queen Anna Maria]
Generated description
Queen Anna Maria is a cruise ship, originally launched as SS Empress of Britain in 1955, that later sailed under the Greek Line after being renamed.
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_69f76e6d3a3c81909db73eda9e0516bd |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f7c733048c8190aa6f5351335b42f4 |
completed | May 3, 2026, 10:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3a09793be88190bd7c0a647185f77f |
completed | June 23, 2026, 4:20 a.m. |
| NEDg | Description generation | batch_6a3a116409dc8190ab992bbbcff526b1 |
completed | June 23, 2026, 4:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a3a151b0e4881908e5404f9095a7ae0 |
completed | June 23, 2026, 5:09 a.m. |
Created at: May 3, 2026, 4:11 p.m.