Triple
T9833392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jaime Lerner |
E239040
|
entity |
| Predicate | numberOfTermsAsGovernorOfParaná |
P17900
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Jaime Lerner, numberOfTermsAsGovernorOfParaná, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTermsAsGovernorOfParaná Context triple: [Jaime Lerner, numberOfTermsAsGovernorOfParaná, 2]
-
A.
numberOfTermsAsGovernor
chosen
Indicates the number of separate terms an individual has served in the role of governor.
-
B.
numberOfGovernors
Indicates the total count of governors associated with or governing a specified entity.
-
C.
maximumNumberOfTermsForGovernor
Indicates the highest number of terms that a governor is allowed to serve in office.
-
D.
succeededInOfficeAsGovernorBy
Indicates that one individual’s term as governor ended and was directly followed by another individual’s term in the same office.
-
E.
hasRegionalGovernor
Indicates that a region is administered or overseen by a specific governor responsible for its governance.
- 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_69ca84e314108190978324a4bdb959f8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb336bfc4819084f0d4d6d1867484 |
completed | April 2, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69cd03e30bc08190816c0a6d29c21b0f |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:32 p.m.