Triple
T36510202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SS Empress of Britain (1977) |
E899878
|
entity |
| Predicate | passengerShipType |
P3141
|
FINISHED |
| Object | passenger liner |
—
|
LITERAL 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: passenger liner | Statement: [SS Empress of Britain (1977), passengerShipType, passenger liner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerShipType Context triple: [SS Empress of Britain (1977), passengerShipType, passenger liner]
-
A.
shipClass
chosen
Indicates the classification or type category to which a particular ship belongs.
-
B.
shipTypeProduced
Indicates that a particular type of ship is produced, built, or manufactured by a given entity.
-
C.
originalShipType
Indicates the type or category of ship that an entity was originally classified or built as.
-
D.
laterShipType
Indicates that one ship type chronologically succeeds or is introduced after another ship type.
-
E.
shipTypeInvolved
Indicates that a particular type or class of ship is involved or participates in a specified event, situation, or relationship.
- F. None of above.
Provenance (3 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_69f76e5dada881909da2d34bc7a9202a |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7cec454a88190a9f3bbee2b856636 |
completed | May 3, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f7c8977c288190997a892ec5f756ed |
completed | May 3, 2026, 10:13 p.m. |
Created at: May 3, 2026, 4:10 p.m.