Triple
T34555179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1983 Nigerian presidential election |
E887182
|
entity |
| Predicate | hasCandidateNumber |
P135676
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [1983 Nigerian presidential election, hasCandidateNumber, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCandidateNumber Context triple: [1983 Nigerian presidential election, hasCandidateNumber, 5]
-
A.
isCandidate
Indicates that an entity is being considered or put forward for a particular position, role, or selection process.
-
B.
hasGivenNumber
Indicates that an entity is associated with or assigned a specific number.
-
C.
hasCompetitorNumber
chosen
Indicates that an entity is associated with a specific competitor identification number used to distinguish it in a competitive context.
-
D.
hasCanonicalNumber
Indicates that an entity is associated with its officially recognized or standard reference number.
-
E.
hasSchoolNumber
Indicates that an entity is associated with a specific school identification number.
- 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_69f349cff89081908f91e0b064f4833e |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff6c061c6c81909ff485e9cafc88a2 |
completed | May 9, 2026, 5:16 p.m. |
| PD | Predicate disambiguation | batch_69ff6aaf886c8190a3c87d089453f3de |
completed | May 9, 2026, 5:11 p.m. |
Created at: May 1, 2026, 2:02 a.m.