Triple
T15305916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Happy-Go-Lucky |
E365896
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Poppy Cross
Poppy Cross is an irrepressibly cheerful and optimistic London schoolteacher whose sunny outlook shapes the story of the film "Happy-Go-Lucky."
|
E1150626
|
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: Poppy Cross | Statement: [Happy-Go-Lucky, mainCharacter, Poppy Cross]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Poppy Cross Context triple: [Happy-Go-Lucky, mainCharacter, Poppy Cross]
-
A.
Poppy
Poppy is a young member of the extended Pritchett family in the television series "Modern Family."
-
B.
Poppy
Poppy is a glamorous love interest in the 1932 gangster film "Scarface," entangled in the dangerous world of crime and ambition surrounding the main character.
-
C.
Poppy
Poppy is the upbeat, pink-haired Troll princess who serves as the optimistic and determined protagonist of the animated film "Trolls."
-
D.
Poppy
Poppy is a feminine given name commonly associated with the bright red flower and often used in English-speaking countries.
-
E.
Poppy Papava
Poppy Papava is a fictional character appearing in the James Bond continuation novel "Devil May Care" by Sebastian Faulks.
- 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: Poppy Cross Triple: [Happy-Go-Lucky, mainCharacter, Poppy Cross]
Generated description
Poppy Cross is an irrepressibly cheerful and optimistic London schoolteacher whose sunny outlook shapes the story of the film "Happy-Go-Lucky."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Poppy Cross Target entity description: Poppy Cross is an irrepressibly cheerful and optimistic London schoolteacher whose sunny outlook shapes the story of the film "Happy-Go-Lucky."
-
A.
Poppy
Poppy is a young member of the extended Pritchett family in the television series "Modern Family."
-
B.
Poppy
Poppy is a glamorous love interest in the 1932 gangster film "Scarface," entangled in the dangerous world of crime and ambition surrounding the main character.
-
C.
Poppy
Poppy is the upbeat, pink-haired Troll princess who serves as the optimistic and determined protagonist of the animated film "Trolls."
-
D.
Poppy
Poppy is a feminine given name commonly associated with the bright red flower and often used in English-speaking countries.
-
E.
Poppy Papava
Poppy Papava is a fictional character appearing in the James Bond continuation novel "Devil May Care" by Sebastian Faulks.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03ccef14c819099c5ebe962e7f867 |
completed | April 16, 2026, 1:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef89d961481909be8dcc2864982c9 |
completed | May 9, 2026, 9:04 a.m. |
| NEDg | Description generation | batch_69fefa339f988190b470e052c853e4f8 |
completed | May 9, 2026, 9:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fefac48df08190ad58e9d455546a57 |
completed | May 9, 2026, 9:13 a.m. |
Created at: April 10, 2026, 3:16 a.m.