Triple
T4705893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cai Guo-Qiang |
E104388
|
entity |
| Predicate | hasExplosiveEvent |
P45831
|
FINISHED |
| Object | explosion event on the Great Wall of China |
—
|
LITERAL 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: explosion event on the Great Wall of China | Statement: [Cai Guo-Qiang, hasExplosiveEvent, explosion event on the Great Wall of China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasExplosiveEvent Context triple: [Cai Guo-Qiang, hasExplosiveEvent, explosion event on the Great Wall of China]
-
A.
numberOfExplosions
Indicates the count of distinct explosion events associated with an entity or situation.
-
B.
explosiveClass
Indicates the classification or category assigned to an explosive based on its type, properties, or regulatory class.
-
C.
hasPyrotechnics
chosen
Indicates that an entity includes, uses, or features pyrotechnic effects or fireworks as part of its characteristics or activities.
-
D.
hadEvent
Indicates that an entity experienced, hosted, or was associated with a specific event at some point in time.
-
E.
hasSideEvent
Indicates that an event is associated with an additional, related side event occurring alongside it.
- F. None of above.
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_69bd43eac3c08190af7e4020c6c3704c |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd650ad0f88190844bfcb46b3071c2 |
completed | March 20, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69bd621ba7448190a53ab1e2897acf71 |
completed | March 20, 2026, 3:04 p.m. |
Created at: March 20, 2026, 1:17 p.m.