Triple
T675411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kim Kardashian |
E13066
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
SKIMS
SKIMS is Kim Kardashian’s shapewear and loungewear brand known for its inclusive sizing, neutral tones, and body-contouring designs.
|
E83327
|
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: SKIMS | Statement: [Kim Kardashian, notableWork, SKIMS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SKIMS Context triple: [Kim Kardashian, notableWork, SKIMS]
-
A.
SKC
SKC is the commonly used abbreviation for Sporting Kansas City, a professional Major League Soccer club based in Kansas City.
-
B.
Pijin
Pijin is an English-based creole language widely used as a lingua franca in the Solomon Islands.
-
C.
Shompen
The Shompen are an isolated indigenous people of Great Nicobar Island, known for their semi-nomadic forest-based lifestyle and limited contact with the outside world.
-
D.
Skagen
Skagen is a minimalist Danish-inspired watch and accessories brand known for its clean design aesthetic and modern, affordable timepieces.
-
E.
Bata
Bata is a major port city on the mainland of Equatorial Guinea, serving as a key economic and transportation hub for the country.
- 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: SKIMS Triple: [Kim Kardashian, notableWork, SKIMS]
Generated description
SKIMS is Kim Kardashian’s shapewear and loungewear brand known for its inclusive sizing, neutral tones, and body-contouring designs.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SKIMS Target entity description: SKIMS is Kim Kardashian’s shapewear and loungewear brand known for its inclusive sizing, neutral tones, and body-contouring designs.
-
A.
SKC
SKC is the commonly used abbreviation for Sporting Kansas City, a professional Major League Soccer club based in Kansas City.
-
B.
Pijin
Pijin is an English-based creole language widely used as a lingua franca in the Solomon Islands.
-
C.
Shompen
The Shompen are an isolated indigenous people of Great Nicobar Island, known for their semi-nomadic forest-based lifestyle and limited contact with the outside world.
-
D.
Skagen
Skagen is a minimalist Danish-inspired watch and accessories brand known for its clean design aesthetic and modern, affordable timepieces.
-
E.
Bata
Bata is a major port city on the mainland of Equatorial Guinea, serving as a key economic and transportation hub for the country.
- 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_69a4933d3bf88190972041cd8cf143b9 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a0266e7c8190a94c4b4b761c59f4 |
completed | March 1, 2026, 8:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5c3a1b6588190b0c9215afb3a9200 |
completed | March 2, 2026, 5:06 p.m. |
| NEDg | Description generation | batch_69a5c7ff50088190827743f3f42622ce |
completed | March 2, 2026, 5:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a5cd1dd7848190a987276500040a4f |
completed | March 2, 2026, 5:47 p.m. |
Created at: March 1, 2026, 7:36 p.m.