Triple
T12066468
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simona Brown |
E287308
|
entity |
| Predicate | appearedIn |
P795
|
FINISHED |
| Object |
Kiss Me First
Kiss Me First is a British cyber-thriller drama television series that blends virtual reality gaming with real-world mystery and danger.
|
E968972
|
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: Kiss Me First | Statement: [Simona Brown, appearedIn, Kiss Me First]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kiss Me First Context triple: [Simona Brown, appearedIn, Kiss Me First]
-
A.
Kiss Me
"Kiss Me" is the smooth, jazz-influenced theme song best known for its use in the opening credits of the classic American sitcom *The Cosby Show*.
-
B.
Kiss Me
"Kiss Me" is a pop single by English singer Olly Murs, known for its catchy, upbeat style and romantic lyrics.
-
C.
Kiss Me
"Kiss Me" is a 1998 romantic pop song by Sixpence None the Richer that became widely known for its prominent use in the teen film "She's All That."
-
D.
Kiss Kiss
"Kiss Kiss" is a 2007 R&B/hip-hop single by Chris Brown featuring T-Pain, known for its catchy hook, dance-focused production, and commercial success on the Billboard charts.
-
E.
Kiss Kiss
Kiss Kiss is a darkly comic short story collection by Roald Dahl, featuring macabre twists and unsettling explorations of human nature.
- 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: Kiss Me First Triple: [Simona Brown, appearedIn, Kiss Me First]
Generated description
Kiss Me First is a British cyber-thriller drama television series that blends virtual reality gaming with real-world mystery and danger.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kiss Me First Target entity description: Kiss Me First is a British cyber-thriller drama television series that blends virtual reality gaming with real-world mystery and danger.
-
A.
Kiss Me
"Kiss Me" is the smooth, jazz-influenced theme song best known for its use in the opening credits of the classic American sitcom *The Cosby Show*.
-
B.
Kiss Me
"Kiss Me" is a pop single by English singer Olly Murs, known for its catchy, upbeat style and romantic lyrics.
-
C.
Kiss Me
"Kiss Me" is a 1998 romantic pop song by Sixpence None the Richer that became widely known for its prominent use in the teen film "She's All That."
-
D.
Kiss Kiss
"Kiss Kiss" is a 2007 R&B/hip-hop single by Chris Brown featuring T-Pain, known for its catchy hook, dance-focused production, and commercial success on the Billboard charts.
-
E.
Kiss Kiss
Kiss Kiss is a darkly comic short story collection by Roald Dahl, featuring macabre twists and unsettling explorations of human nature.
- 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_69d6ab4846e081908ee7bbd66a6d3459 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d904423dc08190a47194422255c62e |
completed | April 10, 2026, 2:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60a66711481909fe0b0d3b2934601 |
completed | May 2, 2026, 2:29 p.m. |
| NEDg | Description generation | batch_69f60bda16e48190af8abc0aa8ef41f0 |
completed | May 2, 2026, 2:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f60cd1668881908f43d895fcfba0aa |
completed | May 2, 2026, 2:40 p.m. |
Created at: April 8, 2026, 9:48 p.m.