Triple
T18819635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rafferty Law |
E460227
|
entity |
| Predicate | hasWorkedWithBrand |
P42926
|
FINISHED |
| Object |
Topman
Topman is a British high-street fashion retailer known for its trend-focused menswear aimed primarily at young adults.
|
E1343983
|
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: Topman | Statement: [Rafferty Law, hasWorkedWithBrand, Topman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Topman Context triple: [Rafferty Law, hasWorkedWithBrand, Topman]
-
A.
Topshop
Topshop is a British fast-fashion retailer known for its trendy, youth-oriented clothing and accessories.
-
B.
Reiss
Reiss is a German-language surname borne by various notable individuals across fields such as mountaineering, arts, and academia.
-
C.
Cotton On
Cotton On is a global Australian retail chain known for its affordable, casual fashion and lifestyle products.
-
D.
Topshop Unique
Topshop Unique was the premium, runway-focused line of the British high-street fashion retailer Topshop, known for showing at London Fashion Week.
-
E.
Ursula Jeans
Ursula Jeans was a British stage and film actress known for her versatile character roles in mid-20th-century cinema and theatre.
- 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: Topman Triple: [Rafferty Law, hasWorkedWithBrand, Topman]
Generated description
Topman is a British high-street fashion retailer known for its trend-focused menswear aimed primarily at young adults.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Topman Target entity description: Topman is a British high-street fashion retailer known for its trend-focused menswear aimed primarily at young adults.
-
A.
Topshop
Topshop is a British fast-fashion retailer known for its trendy, youth-oriented clothing and accessories.
-
B.
Reiss
Reiss is a German-language surname borne by various notable individuals across fields such as mountaineering, arts, and academia.
-
C.
Cotton On
Cotton On is a global Australian retail chain known for its affordable, casual fashion and lifestyle products.
-
D.
Topshop Unique
Topshop Unique was the premium, runway-focused line of the British high-street fashion retailer Topshop, known for showing at London Fashion Week.
-
E.
Ursula Jeans
Ursula Jeans was a British stage and film actress known for her versatile character roles in mid-20th-century cinema and theatre.
- 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_69d8dcf94c288190a06dea029ae4b223 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a6b8b7d88190a8828746176776ea |
completed | April 20, 2026, 4:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a055bca90d481908e1633dc4eef476d |
completed | May 14, 2026, 5:21 a.m. |
| NEDg | Description generation | batch_6a055d68aa508190a2d3d4212aed4858 |
completed | May 14, 2026, 5:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a055e1a22308190bb7f2337fabc5bdf |
completed | May 14, 2026, 5:31 a.m. |
Created at: April 10, 2026, 11:55 a.m.