Triple
T17607216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deadfall |
E428863
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object |
Lou
Lou is a character in the crime thriller film "Deadfall," involved in the movie’s tense, violent family-centered plot.
|
E1277868
|
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: Lou | Statement: [Deadfall, featuresCharacter, Lou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lou Context triple: [Deadfall, featuresCharacter, Lou]
-
A.
Lou
Lou is a recurring Springfield police officer on the animated television series "The Simpsons," known as Chief Wiggum’s level-headed, deadpan partner.
-
B.
Lou
Lou is a central character in the Canadian romantic drama film "Take This Waltz," which explores themes of love, fidelity, and emotional restlessness.
-
C.
Lou
Lou is a common diminutive form of the given name Louise.
-
D.
Lou
Lou is a character from the virtual reality co-op shooter game "After the Fall," set in a post-apocalyptic, frozen Los Angeles overrun by mutated creatures.
-
E.
Lou
Lou is a character in the television miniseries "The Continental: From the World of John Wick," set in the action-packed criminal underworld of the John Wick franchise.
- 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: Lou Triple: [Deadfall, featuresCharacter, Lou]
Generated description
Lou is a character in the crime thriller film "Deadfall," involved in the movie’s tense, violent family-centered plot.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lou Target entity description: Lou is a character in the crime thriller film "Deadfall," involved in the movie’s tense, violent family-centered plot.
-
A.
Lou
Lou is the protagonist of the film "Love Lies Bleeding," a determined and emotionally complex character whose choices drive the story’s dark, romantic crime narrative.
-
B.
Lou
Lou is a supporting character in the romantic drama film "Stuck in Love," involved in the intertwined relationships and personal struggles of a family of writers.
-
C.
Lou
Lou is a central character in the Canadian romantic drama film "Take This Waltz," which explores themes of love, fidelity, and emotional restlessness.
-
D.
Lou
Lou is a character from the virtual reality co-op shooter game "After the Fall," set in a post-apocalyptic, frozen Los Angeles overrun by mutated creatures.
-
E.
Lou
Lou is a character in the television miniseries "The Continental: From the World of John Wick," set in the action-packed criminal underworld of the John Wick franchise.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46c4ccef08190aeaa88670364bd74 |
completed | April 19, 2026, 5:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01e8201678819094fc43c1fcde7203 |
completed | May 11, 2026, 2:30 p.m. |
| NEDg | Description generation | batch_6a01ecfb2ff4819082f67ab1f2ce8885 |
completed | May 11, 2026, 2:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01ee0c03e881908aaaa3bd0f596387 |
completed | May 11, 2026, 2:56 p.m. |
Created at: April 10, 2026, 5:51 a.m.