Triple
T16612543
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Louis to Liverpool |
E403611
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Little Marie
Little Marie is a ship or vessel associated with the transatlantic route between St. Louis and Liverpool.
|
E1223538
|
NE FINISHED |
How this triple was built (4 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: Little Marie | Statement: [St. Louis to Liverpool, hasPart, Little Marie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Little Marie Context triple: [St. Louis to Liverpool, hasPart, Little Marie]
-
A.
Marnie
Marnie is a 1964 psychological thriller film directed by Alfred Hitchcock, starring Tippi Hedren and Sean Connery, about a troubled woman with a mysterious past and compulsive thieving.
-
B.
Marnie
Marnie is the given name of Darcey Bussell, the renowned British ballerina and former principal dancer of The Royal Ballet.
-
C.
Maidie
Maidie is the central character of the television series "Dads," around whom the show's primary storylines and character dynamics revolve.
-
D.
Betsy
Betsy is a common diminutive or nickname for the given name Elizabeth.
-
E.
Betsy
Betsy is a key female character in the 1976 film "Taxi Driver," known as the idealistic campaign worker who becomes the object of Travis Bickle’s fixation.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Little Marie Triple: [St. Louis to Liverpool, hasPart, Little Marie]
Generated description
Little Marie is a ship or vessel associated with the transatlantic route between St. Louis and Liverpool.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Little Marie Target entity description: Little Marie is a ship or vessel associated with the transatlantic route between St. Louis and Liverpool.
-
A.
Marnie
Marnie is a 1964 psychological thriller film directed by Alfred Hitchcock, starring Tippi Hedren and Sean Connery, about a troubled woman with a mysterious past and compulsive thieving.
-
B.
Marnie
Marnie is the given name of Darcey Bussell, the renowned British ballerina and former principal dancer of The Royal Ballet.
-
C.
Maidie
Maidie is the central character of the television series "Dads," around whom the show's primary storylines and character dynamics revolve.
-
D.
Betsy
Betsy is a common diminutive or nickname for the given name Elizabeth.
-
E.
Betsy
Betsy is a key female character in the 1976 film "Taxi Driver," known as the idealistic campaign worker who becomes the object of Travis Bickle’s fixation.
- F. None of above. chosen
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_69d883880d0c81908b5fcd454e767b60 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3609776d48190b6b8c7826ac575c4 |
completed | April 18, 2026, 10:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0075aeaa9881908bdef0f9f2b52e60 |
completed | May 10, 2026, 12:10 p.m. |
| NEDg | Description generation | batch_6a007705f57881908b07a20ae8957c64 |
completed | May 10, 2026, 12:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a007b18f0b08190a9ddc6ad7358d6b8 |
completed | May 10, 2026, 12:33 p.m. |
Created at: April 10, 2026, 5:17 a.m.