Triple
T18516911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hankyu Department Store |
E452488
|
entity |
| Predicate | hasBrand |
P1500
|
FINISHED |
| Object |
Hankyu Beauty
Hankyu Beauty is a cosmetics and beauty retail brand associated with Japan’s Hankyu Department Store, offering a curated selection of skincare, makeup, and fragrance products.
|
E1328870
|
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: Hankyu Beauty | Statement: [Hankyu Department Store, hasBrand, Hankyu Beauty]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hankyu Beauty Context triple: [Hankyu Department Store, hasBrand, Hankyu Beauty]
-
A.
Yokoshibahikari
Yokoshibahikari is a coastal town in Chiba Prefecture, Japan, known for encompassing part of the long sandy shoreline of Kujūkuri Beach along the Pacific Ocean.
-
B.
Hana
Hana is a common female given name of Hebrew origin, often associated with meanings like "grace" or "favor."
-
C.
Hana
Hana is a studio album by English singer-songwriter Sophie Ellis-Bextor that showcases her blend of dance-pop and introspective, melodic songwriting.
-
D.
Hana
Hana is a person known primarily as the romantic partner of Kip.
-
E.
Hana
Hana is a Japanese restaurant located within Tokyo Disney Resort’s Disney Ambassador Hotel, offering themed dining to hotel guests and park visitors.
- 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: Hankyu Beauty Triple: [Hankyu Department Store, hasBrand, Hankyu Beauty]
Generated description
Hankyu Beauty is a cosmetics and beauty retail brand associated with Japan’s Hankyu Department Store, offering a curated selection of skincare, makeup, and fragrance products.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hankyu Beauty Target entity description: Hankyu Beauty is a cosmetics and beauty retail brand associated with Japan’s Hankyu Department Store, offering a curated selection of skincare, makeup, and fragrance products.
-
A.
Yokoshibahikari
Yokoshibahikari is a coastal town in Chiba Prefecture, Japan, known for encompassing part of the long sandy shoreline of Kujūkuri Beach along the Pacific Ocean.
-
B.
Hana
Hana is a common female given name of Hebrew origin, often associated with meanings like "grace" or "favor."
-
C.
Hana
Hana is a studio album by English singer-songwriter Sophie Ellis-Bextor that showcases her blend of dance-pop and introspective, melodic songwriting.
-
D.
Hana
Hana is a person known primarily as the romantic partner of Kip.
-
E.
Hana
Hana is a Japanese restaurant located within Tokyo Disney Resort’s Disney Ambassador Hotel, offering themed dining to hotel guests and park visitors.
- 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_69d8d386df84819092355ebb260d848e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5338b2cd0819095db59f6bfc70814 |
completed | April 19, 2026, 7:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a04916c1cb88190889ad7d1c1dc4279 |
completed | May 13, 2026, 2:57 p.m. |
| NEDg | Description generation | batch_6a04921b1a508190b8bb6d6c4597ab99 |
completed | May 13, 2026, 3 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0492ba4ea08190b59510b221ad5d21 |
completed | May 13, 2026, 3:03 p.m. |
Created at: April 10, 2026, 11:36 a.m.