Triple
T9656276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SCUT |
E233459
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | SCUT |
E233459
|
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: SCUT | Statement: [SCUT, abbreviation, SCUT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SCUT Context triple: [SCUT, abbreviation, SCUT]
-
A.
SCUT
chosen
SCUT is a major public research university in Guangzhou, China, known for its strong engineering, technology, and applied science programs.
-
B.
SCAU
SCAU is a French architectural firm known for designing major public and sports facilities, including the redevelopment of Marseille’s Stade Vélodrome.
-
C.
Sichuan University
Sichuan University is a major comprehensive research university in Chengdu, China, known for its wide range of academic disciplines and strong national reputation.
-
D.
SWUFE
SWUFE is a leading Chinese university specializing in finance, economics, and business education and research.
-
E.
HUST
HUST is a leading Vietnamese technical university renowned for its engineering, science, and technology education and research.
- 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_69ca848c1ba88190b84b410cd14627fc |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9bdd5c0c8190a6c82a1609454d1b |
completed | April 1, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d18a07444c819099d7462c38f6da49 |
completed | April 4, 2026, 10 p.m. |
Created at: March 30, 2026, 8:14 p.m.