Triple
T38434821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Squadron |
E903910
|
entity |
| Predicate | typicalSubUnits |
P71417
|
FINISHED |
| Object | troops |
—
|
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: troops | Statement: [A Squadron, typicalSubUnits, troops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSubUnits Context triple: [A Squadron, typicalSubUnits, troops]
-
A.
typicalSubunitOf
Indicates that something is a standard or commonly occurring subcomponent or part of a larger whole.
-
B.
subunitType
Indicates that one entity is a specific kind or classification of subunit within the structure or composition of another entity.
-
C.
subunitOf
Indicates that one entity functions as a component or smaller part within the structure or organization of another, larger entity.
-
D.
typicalSubmultiples
Indicates that one quantity represents a standard or commonly used fractional multiple of another quantity (e.g., milli-, micro-, kilo- as typical submultiples).
-
E.
hasSubunits
chosen
Indicates that an entity is composed of or organized into smaller constituent units that are part of its structure.
- 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_69f76e6a2024819081aa04f4932f89d2 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1e32108190897356d6a7fed879 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:31 p.m.