Triple
T2229618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Swire Pacific |
E48733
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object | Taikoo |
E245416
|
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: Taikoo | Statement: [Swire Pacific, brand, Taikoo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taikoo Context triple: [Swire Pacific, brand, Taikoo]
-
A.
Taikoo Sugar
chosen
Taikoo Sugar is a historic Hong Kong-based sugar refining and food brand known for its packaged sugars and related products, originally developed under the Swire conglomerate.
-
B.
Tabio
Tabio is a small Colombian town in the department of Cundinamarca, known for its cool climate, agricultural traditions, and proximity to Bogotá.
-
C.
Kierling
Kierling is a small locality in Lower Austria best known as the place where writer Franz Kafka spent his final days and died.
-
D.
Tenjin
Tenjin is the Shinto kami of scholarship and learning, widely revered by students seeking academic success.
-
E.
Osan
Osan is a city in Gyeonggi Province, South Korea, known for its proximity to Osan Air Base and its role as a regional transportation and commercial hub.
- 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_69a88aa51b388190949868ec9766e587 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc069e0ac8190bcda8cba9f5c7a5d |
completed | March 7, 2026, 6:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6afe4d1481908c6c27e889303892 |
completed | March 9, 2026, 6:38 a.m. |
Created at: March 4, 2026, 7:47 p.m.