Triple
T13792340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waist Deep |
E331428
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Lucky
Lucky is a character in the crime drama film "Waist Deep," involved in the gritty, high-stakes world surrounding the protagonist.
|
E1061231
|
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: Lucky | Statement: [Waist Deep, character, Lucky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lucky Context triple: [Waist Deep, character, Lucky]
-
A.
Lucky
"Lucky" is a popular Afrobeats/hip-hop song by Ghanaian rapper Sarkodie, known for its smooth blend of rap and melodic vocals.
-
B.
Lucky
"Lucky" is a popular duet by American singers Colbie Caillat and Jason Mraz, known for its mellow acoustic pop style and romantic lyrics.
-
C.
Lucky
Lucky is a regional supermarket chain brand in the United States known for its neighborhood grocery stores and value-focused offerings.
-
D.
Lucky
Lucky is a bestselling novel by Jackie Collins that follows the glamorous, ruthless world of Lucky Santangelo in the high-stakes realms of power, sex, and crime.
-
E.
Lucky
"Lucky" is a song by the English rock band Radiohead, featured on their acclaimed 1997 album OK Computer.
- 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: Lucky Triple: [Waist Deep, character, Lucky]
Generated description
Lucky is a character in the crime drama film "Waist Deep," involved in the gritty, high-stakes world surrounding the protagonist.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lucky Target entity description: Lucky is a character in the crime drama film "Waist Deep," involved in the gritty, high-stakes world surrounding the protagonist.
-
A.
Lucky
Lucky is the protagonist of the 1993 film "Poetic Justice," portrayed by Tupac Shakur as a sensitive and complex mail carrier navigating love, grief, and self-discovery.
-
B.
Lucky
Lucky is a tormented, subservient figure in Samuel Beckett’s play "Waiting for Godot," known for his near-muteness and one explosive, chaotic monologue that reflects the play’s themes of absurdity and existential despair.
-
C.
Lucky
Lucky is a bestselling novel by Jackie Collins that follows the glamorous, ruthless world of Lucky Santangelo in the high-stakes realms of power, sex, and crime.
-
D.
Lucky
Lucky is a contemplative 2017 independent film starring Harry Dean Stanton as a nonagenarian atheist confronting mortality in a small desert town.
-
E.
Lucky
Lucky is a recurring dog character in the animated children's series "Bluey," known as Bluey's sporty next-door neighbor and friend.
- 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_69d81c58feb08190a77bca8bf7d6d20f |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0258a1408190a837d17c6d6a2bd4 |
completed | April 14, 2026, 9:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b0817bd88190a46e539f2ff24d83 |
completed | May 3, 2026, 8:30 p.m. |
| NEDg | Description generation | batch_69f7b14fe1cc8190b1a5f6f0e80b7e39 |
completed | May 3, 2026, 8:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7b20f67048190a641527353e3ff43 |
completed | May 3, 2026, 8:37 p.m. |
Created at: April 9, 2026, 10:11 p.m.