Triple
T1575977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taro |
E33651
|
entity |
| Predicate | semanticComponent |
P16024
|
FINISHED |
| Object | "rō" (郎) meaning "son" or "man" |
—
|
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: "rō" (郎) meaning "son" or "man" | Statement: [Taro, semanticComponent, "rō" (郎) meaning "son" or "man"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: semanticComponent Context triple: [Taro, semanticComponent, "rō" (郎) meaning "son" or "man"]
-
A.
meaningComponent
chosen
Indicates that one entity represents a semantic or conceptual component contributing to the overall meaning of another entity.
-
B.
semanticType
Indicates that something belongs to or is categorized under a particular semantic class or type based on its meaning.
-
C.
semanticRootMeaning
Indicates the fundamental or core meaning that underlies a word, phrase, or expression in a semantic structure.
-
D.
semanticRelation
Indicates a general meaning-based connection between two entities, such as similarity, implication, or conceptual association.
-
E.
specialComposition
Indicates that one entity is composed of another in a distinctive or non-standard way, highlighting a particular or exceptional form of composition between them.
- 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_69a885f27a4c8190a4622252cdf54c00 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a96083e7308190abbf025fe8e43abb |
completed | March 5, 2026, 10:52 a.m. |
| PD | Predicate disambiguation | batch_69a907ba63c88190b60c14dec8d1e40f |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:27 p.m.