Triple
T13580763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maria |
E324409
|
entity |
| Predicate | basedOnRealWorldContext |
P2919
|
FINISHED |
| Object | 17th-century English theatre |
—
|
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: 17th-century English theatre | Statement: [Maria, basedOnRealWorldContext, 17th-century English theatre]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnRealWorldContext Context triple: [Maria, basedOnRealWorldContext, 17th-century English theatre]
-
A.
influencedByRealWorldConcept
Indicates that something is shaped, inspired, or determined by an existing concept, phenomenon, or principle from the real world.
-
B.
contextOf
chosen
Indicates that one entity provides the situational, informational, or environmental background within which another entity exists, occurs, or is interpreted.
-
C.
basedOnRealLife
Indicates that something is derived from, inspired by, or directly adapted from actual real-world events, people, or situations.
-
D.
basedOnRealRegion
Indicates that something is derived from, inspired by, or corresponds to an actual geographic or administrative region in the real world.
-
E.
soldInRealWorld
Indicates that the item or product is actually sold or available for purchase in the physical, real-world marketplace.
- 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_69d80769100c819099111274614f5ed2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb03052088190a2b68c106059828e |
completed | April 12, 2026, 2:46 p.m. |
| PD | Predicate disambiguation | batch_69dbae161a0481909f9d3f40ca4e0ac5 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:48 p.m.