Triple
T14413641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Love Lies Bleeding |
E357391
|
entity |
| Predicate | characterOccupation |
P268
|
FINISHED |
| Object |
Lou is a gym manager
Lou is a central character in the film "Love Lies Bleeding," portrayed as a tough, determined gym manager whose life becomes entangled in a violent, crime-laced love story.
|
E1097686
|
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 is a gym manager | Statement: [Love Lies Bleeding, characterOccupation, Lou is a gym manager]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lou is a gym manager Context triple: [Love Lies Bleeding, characterOccupation, Lou is a gym manager]
-
A.
LOU
LOU is the standard sports abbreviation used to represent the Louisville Panthers team.
-
B.
LOU
LOU is the standard abbreviation used for the Louisville Bats, a Minor League Baseball team based in Louisville, Kentucky.
-
C.
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.
-
D.
Lou
Lou is a common diminutive form of the given name Louise.
-
E.
Lou
Lou is a recurring Springfield police officer on the animated television series "The Simpsons," known as Chief Wiggum’s level-headed, deadpan partner.
- 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 is a gym manager Triple: [Love Lies Bleeding, characterOccupation, Lou is a gym manager]
Generated description
Lou is a central character in the film "Love Lies Bleeding," portrayed as a tough, determined gym manager whose life becomes entangled in a violent, crime-laced love story.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lou is a gym manager Target entity description: Lou is a central character in the film "Love Lies Bleeding," portrayed as a tough, determined gym manager whose life becomes entangled in a violent, crime-laced love story.
-
A.
LOU
LOU is the standard sports abbreviation used to represent the Louisville Panthers team.
-
B.
LOU
LOU is the standard abbreviation used for the Louisville Bats, a Minor League Baseball team based in Louisville, Kentucky.
-
C.
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.
-
D.
Lou
Lou is a common diminutive form of the given name Louise.
-
E.
Lou
Lou is a recurring Springfield police officer on the animated television series "The Simpsons," known as Chief Wiggum’s level-headed, deadpan partner.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90cb3c708190822f5506ebf7ee9d |
completed | April 14, 2026, 7:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd552858208190ba1550e7c1176a2a |
completed | May 8, 2026, 3:14 a.m. |
| NEDg | Description generation | batch_69fd5671e4688190ab1b7a7ed6c0cfb8 |
completed | May 8, 2026, 3:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd57710f648190a1344ac1363acce1 |
completed | May 8, 2026, 3:24 a.m. |
Created at: April 10, 2026, 1:17 a.m.