Triple
T20561866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jonathan Brandis |
E504861
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Ladybugs
Ladybugs is a 1992 sports comedy film in which Jonathan Brandis plays a boy who disguises himself as a girl to help a struggling girls' soccer team.
|
E1438619
|
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: Ladybugs | Statement: [Jonathan Brandis, notableWork, Ladybugs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ladybugs Context triple: [Jonathan Brandis, notableWork, Ladybugs]
-
A.
Ladybug
Ladybug is the unlucky but introspective assassin protagonist played by Brad Pitt in the 2022 action-comedy film "Bullet Train."
-
B.
Ladybird
Ladybird is a British children’s television series that follows the adventures of a small, friendly ladybird character in whimsical, educational stories.
-
C.
The Grouchy Ladybug
The Grouchy Ladybug is a popular children's picture book by Eric Carle that follows a bad-tempered ladybug learning about manners and sharing over the course of a day.
-
D.
Buzzy Beetle
Buzzy Beetle is a hard-shelled, fireproof enemy from the Super Mario series that typically walks along surfaces and retreats into its shell when jumped on.
-
E.
Lightning Bugs
The Lightning Bugs are the athletic mascot representing Lehman College’s sports teams.
- 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: Ladybugs Triple: [Jonathan Brandis, notableWork, Ladybugs]
Generated description
Ladybugs is a 1992 sports comedy film in which Jonathan Brandis plays a boy who disguises himself as a girl to help a struggling girls' soccer team.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ladybugs Target entity description: Ladybugs is a 1992 sports comedy film in which Jonathan Brandis plays a boy who disguises himself as a girl to help a struggling girls' soccer team.
-
A.
Ladybug
Ladybug is the unlucky but introspective assassin protagonist played by Brad Pitt in the 2022 action-comedy film "Bullet Train."
-
B.
Ladybird
Ladybird is a British children’s television series that follows the adventures of a small, friendly ladybird character in whimsical, educational stories.
-
C.
The Grouchy Ladybug
The Grouchy Ladybug is a popular children's picture book by Eric Carle that follows a bad-tempered ladybug learning about manners and sharing over the course of a day.
-
D.
Buzzy Beetle
Buzzy Beetle is a hard-shelled, fireproof enemy from the Super Mario series that typically walks along surfaces and retreats into its shell when jumped on.
-
E.
Lightning Bugs
The Lightning Bugs are the athletic mascot representing Lehman College’s sports teams.
- 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_69e0b4b6587c8190aee63dc7cff244ea |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a79ec10481909740eb6a08ae2658 |
completed | April 20, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08acdac8008190a7a61d46f7041cf4 |
completed | May 16, 2026, 5:43 p.m. |
| NEDg | Description generation | batch_6a08ada4ef648190982235ebe059a9c5 |
completed | May 16, 2026, 5:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08aeaf2be88190bac218c7de1544fc |
completed | May 16, 2026, 5:51 p.m. |
Created at: April 16, 2026, 11:39 a.m.