Triple

T25102690
Position Surface form Disambiguated ID Type / Status
Subject Asok Station E628773 entity
Predicate nearShoppingArea P61270 FINISHED
Object Sukhumvit retail corridor
The Sukhumvit retail corridor is a major commercial strip in Bangkok known for its dense concentration of shopping malls, boutiques, restaurants, and nightlife venues along Sukhumvit Road.
 E1663279 NE FINISHED

How this triple was built (3 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: Sukhumvit retail corridor | Statement: [Asok Station, nearShoppingArea, Sukhumvit retail corridor]
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: Sukhumvit retail corridor
Triple: [Asok Station, nearShoppingArea, Sukhumvit retail corridor]
Generated description
The Sukhumvit retail corridor is a major commercial strip in Bangkok known for its dense concentration of shopping malls, boutiques, restaurants, and nightlife venues along Sukhumvit Road.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: nearShoppingArea
Context triple: [Asok Station, nearShoppingArea, Sukhumvit retail corridor]
  • A. nearbyUse
    Indicates that one entity uses or operates another entity that is located nearby or in close physical proximity.
  • B. nearbyLocation chosen
    Indicates that one location is situated close to another location in physical space.
  • C. nearbyUrbanCenter
    Indicates that one location is geographically close to an urban center, such as a city or large town.
  • D. locationOfShoppingCenter
    Indicates that a specified place is the geographic location where a particular shopping center is situated.
  • E. nearbyVenue
    Indicates that one venue is located close to another venue in physical space.
  • F. None of above.

Provenance (6 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_69e2ff3071548190b62d1ac237397197 completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f464be45448190b42c7d880d8550c8 completed May 1, 2026, 8:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1048f02a308190adf7e34caf827666 completed May 22, 2026, 12:15 p.m.
NEDg Description generation batch_6a104a6df4208190b8fa9647b516b7fc completed May 22, 2026, 12:22 p.m.
NED2 Entity disambiguation (via description) batch_6a104c2d8308819097b21b979944585e completed May 22, 2026, 12:29 p.m.
PD Predicate disambiguation batch_69f45cfb53f4819099bba48c5057e787 completed May 1, 2026, 7:57 a.m.
Created at: April 18, 2026, 6:26 a.m.