Triple
T12858670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Cordon Bleu |
E307525
|
entity |
| Predicate | hasCampusIn |
P4623
|
FINISHED |
| Object | Bangkok |
E10237
|
NE FINISHED |
How this triple was built (2 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: Bangkok | Statement: [Le Cordon Bleu, hasCampusIn, Bangkok]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bangkok Context triple: [Le Cordon Bleu, hasCampusIn, Bangkok]
-
A.
Bangkok
chosen
Bangkok is the vibrant capital and largest city of Thailand, known for its bustling street life, ornate temples, and role as a major economic and cultural hub in Southeast Asia.
-
B.
Pattaya
Pattaya is a major Thai coastal city known for its vibrant nightlife, beaches, and role as a leading international tourist resort.
-
C.
Hat Yai
Hat Yai is a major commercial and transportation hub city in southern Thailand, known for its bustling markets and proximity to the Malaysian border.
-
D.
Chiang Mai
Chiang Mai is a historic city in northern Thailand known for its ancient temples, vibrant night markets, and surrounding mountainous landscapes.
-
E.
Seoul–Bangkok
Seoul–Bangkok is a major international air route connecting the capital cities of South Korea and Thailand, popular for both tourism and business travel.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d7bdf5e7cc8190be357278bc5ba3bb |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970242bd48190941cbae0315ebc3d |
completed | April 10, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69b86935c8190835f6a407be52ae3 |
completed | May 3, 2026, 12:49 a.m. |
Created at: April 9, 2026, 5:37 p.m.