Triple
T1673175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | People's Party (Spain) |
E36169
|
entity |
| Predicate | hasBeenInGovernmentSince |
P18957
|
FINISHED |
| Object | 1996 |
—
|
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: 1996 | Statement: [People's Party (Spain), hasBeenInGovernmentSince, 1996]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBeenInGovernmentSince Context triple: [People's Party (Spain), hasBeenInGovernmentSince, 1996]
-
A.
timeInOfficeBeginsIn
chosen
Indicates the point in time or date when an entity’s term, tenure, or period in office starts.
-
B.
servedAsGovernorUntil
Indicates that an entity held the position of governor up to a specified end date or time.
-
C.
succeededInOffice
Indicates that one officeholder directly followed another in holding the same official position.
-
D.
presidentSince
Indicates that one entity has held the office of president of another entity starting from a specified point in time.
-
E.
servedInCabinetOf
Indicates that one person held a position as a member of the governmental cabinet led by another person.
- 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_69a8861286808190939afff3ce8ee31e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69ab272a653481908f48aa1eed5de8a4 |
completed | March 6, 2026, 7:12 p.m. |
| PD | Predicate disambiguation | batch_69aa61b2f6288190b2348ef7d7e4672d |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:29 p.m.