Triple
T1312348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johanus |
E28019
|
entity |
| Predicate | hasNameLengthCategory |
P26903
|
FINISHED |
| Object | medium-length given name |
—
|
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: medium-length given name | Statement: [Johanus, hasNameLengthCategory, medium-length given name]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNameLengthCategory Context triple: [Johanus, hasNameLengthCategory, medium-length given name]
-
A.
hasNumberCategory
Indicates that an entity is associated with a specific numerical classification or type.
-
B.
hasRunwayLengthCategory
Indicates that an airport or airfield is associated with a specific categorical range of runway lengths (e.g., short, medium, long).
-
C.
hasNameAbbreviation
Indicates that an entity is associated with a shortened or abbreviated form of its full name.
-
D.
hasUnicodeName
Indicates that an entity is associated with a specific official Unicode name assigned to a character or symbol.
-
E.
hasLatinName
Indicates that an entity is associated with a specific Latin (scientific) name.
- F. None of above. chosen
Provenance (4 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_69a498532c3481909223b74af2e578df |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1572b1c8190ab978198c2d655c8 |
completed | March 1, 2026, 10:44 p.m. |
| PD | Predicate disambiguation | batch_69a4beebcb348190964bd7215811942c |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4bfc2134c81909cbaaa151d96e9a8 |
completed | March 1, 2026, 10:37 p.m. |
Created at: March 1, 2026, 7:55 p.m.