Triple
T21650247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lotte World Tower |
E534317
|
entity |
| Predicate | developer |
P73
|
FINISHED |
| Object | Lotte Group |
—
|
NE NERFINISHED |
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: Lotte Group | Statement: [Lotte World Tower, developer, Lotte Group]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lotte Group Context triple: [Lotte World Tower, developer, Lotte Group]
-
A.
Lotte Group
chosen
Lotte Group is a major South Korean-Japanese multinational conglomerate with diverse businesses spanning food, retail, tourism, chemicals, and entertainment.
-
B.
Shinsegae Group
Shinsegae Group is a major South Korean retail conglomerate best known for its department stores, supermarkets, and diverse consumer-focused businesses.
-
C.
SK Group
SK Group is one of South Korea’s largest conglomerates, with diversified businesses spanning energy, telecommunications, semiconductors, and chemicals.
-
D.
Hanwha Group
Hanwha Group is a major South Korean conglomerate with diversified businesses spanning chemicals, energy, defense, finance, and construction.
-
E.
Daewoo Group
Daewoo Group was a major South Korean conglomerate (chaebol) that operated across industries such as automobiles, shipbuilding, electronics, and construction before its collapse during the Asian financial crisis.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c466aec88190ba39c7543dbc8ba2 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef5913cd9c81908a6ce9bc741416bf |
completed | April 27, 2026, 12:39 p.m. |
Created at: April 16, 2026, 6:35 p.m.