Triple
T13775112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German federal election, 1928 |
E330984
|
entity |
| Predicate | largestPartySeatsWon |
P31127
|
FINISHED |
| Object | 153 |
—
|
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: 153 | Statement: [German federal election, 1928, largestPartySeatsWon, 153]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: largestPartySeatsWon Context triple: [German federal election, 1928, largestPartySeatsWon, 153]
-
A.
largestParty
Indicates that the subject is the political party with the greatest size (e.g., by membership, seats, or votes) within a specified context or group.
-
B.
numberOfSeatsWon
chosen
Indicates the quantity of seats secured by an entity (such as a party or candidate) in an election or representative body.
-
C.
oppositionPartySeatsWon
Indicates the number of legislative seats secured by the opposition party in an election or governing body.
-
D.
speakerSeatsWon
Indicates the number of seats won by the entity serving or designated as the speaker in a given election or legislative context.
-
E.
largestPartyLeader
Indicates that the subject is the leader of the largest political party within a given political body or context.
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0238bdbc8190a946e6e5431632a5 |
completed | April 14, 2026, 9 a.m. |
| PD | Predicate disambiguation | batch_69dbbe97846c819093b00ea117b64e0d |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 10:10 p.m.