Triple
T21795736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DBApparel |
E538088
|
entity |
| Predicate | brandPortfolioIncludes |
P18121
|
FINISHED |
| Object |
DIM
DIM is a French clothing brand best known for its innovative and stylish underwear, hosiery, and lingerie.
|
E1502969
|
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: DIM | Statement: [DBApparel, brandPortfolioIncludes, DIM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DIM Context triple: [DBApparel, brandPortfolioIncludes, DIM]
-
A.
DIM
DIM is the commonly used abbreviation for Deportivo Independiente Medellín, a professional football club based in Medellín, Colombia.
-
B.
DI
DI is the abbreviation for Defence Intelligence, the United Kingdom’s military intelligence organization responsible for providing strategic and operational intelligence to the government and armed forces.
-
C.
DEN
DEN is the three-letter IATA airport code for Denver International Airport, the primary commercial airport serving Denver, Colorado.
-
D.
DM
DM is the specially designed docking module that enabled Space Shuttle orbiters to safely dock with the Russian Mir space station during the Shuttle–Mir program.
-
E.
DM
DM is the abbreviated name commonly used for the Department of Management in an academic or organizational context.
- 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: DIM Triple: [DBApparel, brandPortfolioIncludes, DIM]
Generated description
DIM is a French clothing brand best known for its innovative and stylish underwear, hosiery, and lingerie.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: DIM Target entity description: DIM is a French clothing brand best known for its innovative and stylish underwear, hosiery, and lingerie.
-
A.
DIM
DIM is the commonly used abbreviation for Deportivo Independiente Medellín, a professional football club based in Medellín, Colombia.
-
B.
DI
DI is the abbreviation for Defence Intelligence, the United Kingdom’s military intelligence organization responsible for providing strategic and operational intelligence to the government and armed forces.
-
C.
DEN
DEN is the three-letter IATA airport code for Denver International Airport, the primary commercial airport serving Denver, Colorado.
-
D.
DM
DM is the specially designed docking module that enabled Space Shuttle orbiters to safely dock with the Russian Mir space station during the Shuttle–Mir program.
-
E.
DM
DM is the vehicle registration code used on license plates for the town of Demmin in Germany.
- 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_69e0c4733f4081909a86622e7e6d15d2 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f06223ecc48190bd3b173586ea7818 |
completed | April 28, 2026, 7:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a3e7e3b508190bccf7bfd6856861a |
completed | May 17, 2026, 10:17 p.m. |
| NEDg | Description generation | batch_6a0a4292891c8190a9d6202faa3eeb95 |
completed | May 17, 2026, 10:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a433f4528819099527393c5966cbb |
completed | May 17, 2026, 10:37 p.m. |
Created at: April 16, 2026, 6:53 p.m.