Triple
T12663157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Italian general election, 2001 |
E302473
|
entity |
| Predicate | politicalSpectrumMainLoser |
P7253
|
FINISHED |
| Object | centre-left |
—
|
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: centre-left | Statement: [Italian general election, 2001, politicalSpectrumMainLoser, centre-left]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: politicalSpectrumMainLoser Context triple: [Italian general election, 2001, politicalSpectrumMainLoser, centre-left]
-
A.
popularVoteLoser
Indicates that the subject became the winner of an election despite receiving fewer popular votes than at least one opponent.
-
B.
popularVoteLoserParty
Indicates that the subject is the political party of a candidate who lost the popular vote in an election.
-
C.
politicalSide
Indicates the political alignment or ideological position that one entity holds in relation to political spectra or groupings.
-
D.
politicalCategory
Indicates the political classification or ideological grouping that an entity belongs to or is associated with.
-
E.
politicalSpectrumPosition
chosen
Indicates the relative ideological placement of an entity along a political spectrum (e.g., left–right, liberal–conservative).
- 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_69d7bded71a88190bb76e2413af9ea66 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9617c5b888190b37d4ede139bb49e |
completed | April 10, 2026, 8:45 p.m. |
| PD | Predicate disambiguation | batch_69d960b78ce8819091f15dd5013e6da5 |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:19 p.m.