Triple
T14319124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German federal election, 1930 |
E355033
|
entity |
| Predicate | DVPSeatsWon |
P31127
|
FINISHED |
| Object | 30 |
—
|
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: 30 | Statement: [German federal election, 1930, DVPSeatsWon, 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: DVPSeatsWon Context triple: [German federal election, 1930, DVPSeatsWon, 30]
-
A.
numberOfSeatsWon
chosen
Indicates the quantity of seats secured by an entity (such as a party or candidate) in an election or representative body.
-
B.
speakerSeatsWon
Indicates the number of seats won by the entity serving or designated as the speaker in a given election or legislative context.
-
C.
sdlpSeatsWon
Indicates the number of seats won by the SDLP in an election or legislative body.
-
D.
otherPartiesSeatsWon
Indicates the number of seats won by all parties other than the primary or main party in an election or representative body.
-
E.
LabourSeatsWon
Indicates the number of parliamentary seats won by the Labour Party in an election.
- 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_69d8278ed42c8190b9f882dcce611347 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de883a93548190ac671b001d15d0bd |
completed | April 14, 2026, 6:32 p.m. |
| PD | Predicate disambiguation | batch_69de2a9515f4819081aabf251bca5878 |
completed | April 14, 2026, 11:52 a.m. |
Created at: April 10, 2026, 1:13 a.m.