Triple
T17878139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sophie Ellis-Bextor |
E447010
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Catch You
"Catch You" is a 2007 uptempo pop single by English singer Sophie Ellis-Bextor, known for its catchy chorus and dance-pop production.
|
E1293449
|
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: Catch You | Statement: [Sophie Ellis-Bextor, notableWork, Catch You]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Catch You Context triple: [Sophie Ellis-Bextor, notableWork, Catch You]
-
A.
I'll Catch You
"I'll Catch You" is an emotional indie rock ballad by The Get Up Kids, known as a fan-favorite closer from their influential early-2000s album "Something to Write Home About."
-
B.
Catch Me
"Catch Me" is a crime thriller novel by Lisa Gardner featuring detective D.D. Warren as she investigates a woman's chilling prediction of her own imminent murder.
-
C.
Catch Me
"Catch Me" is a song by the band Mother Twilight, showcasing their style and musical identity.
-
D.
Pray You Catch Me
"Pray You Catch Me" is the atmospheric, emotionally raw opening track of Beyoncé's visual album *Lemonade*, setting the tone with themes of suspicion, betrayal, and vulnerability.
-
E.
Got You
"Got You" is a house music track by French DJ and producer Michael Calfan, known for its uplifting, melodic style.
- 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: Catch You Triple: [Sophie Ellis-Bextor, notableWork, Catch You]
Generated description
"Catch You" is a 2007 uptempo pop single by English singer Sophie Ellis-Bextor, known for its catchy chorus and dance-pop production.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Catch You Target entity description: "Catch You" is a 2007 uptempo pop single by English singer Sophie Ellis-Bextor, known for its catchy chorus and dance-pop production.
-
A.
I'll Catch You
"I'll Catch You" is an emotional indie rock ballad by The Get Up Kids, known as a fan-favorite closer from their influential early-2000s album "Something to Write Home About."
-
B.
Catch Me
"Catch Me" is a crime thriller novel by Lisa Gardner featuring detective D.D. Warren as she investigates a woman's chilling prediction of her own imminent murder.
-
C.
Catch Me
"Catch Me" is a song by the band Mother Twilight, showcasing their style and musical identity.
-
D.
Pray You Catch Me
"Pray You Catch Me" is the atmospheric, emotionally raw opening track of Beyoncé's visual album *Lemonade*, setting the tone with themes of suspicion, betrayal, and vulnerability.
-
E.
Got You
"Got You" is a house music track by French DJ and producer Michael Calfan, known for its uplifting, melodic style.
- 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_69d8b9f4c22c819093c2680434472894 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49c0c46108190b8edef2572b5ba90 |
completed | April 19, 2026, 9:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0313d1c1e48190a17e3b81c4ee22f3 |
completed | May 12, 2026, 11:49 a.m. |
| NEDg | Description generation | batch_6a031485c3908190a8fe49068d45e910 |
completed | May 12, 2026, 11:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03151de8408190a4c8bc30f5606951 |
completed | May 12, 2026, 11:55 a.m. |
Created at: April 10, 2026, 10:18 a.m.