Triple
T18936381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 3rd Battalion, 26th Marines |
E463257
|
entity |
| Predicate | standardSubunits |
P112029
|
FINISHED |
| Object | rifle companies |
—
|
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: rifle companies | Statement: [3rd Battalion, 26th Marines, standardSubunits, rifle companies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: standardSubunits Context triple: [3rd Battalion, 26th Marines, standardSubunits, rifle companies]
-
A.
typicalSubunitOf
chosen
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.
minorUnitSubdivisions
Indicates that one administrative or organizational unit is subdivided into smaller, subordinate units.
-
D.
hasSubunits
Indicates that an entity is composed of or organized into smaller constituent units that are part of its structure.
-
E.
typicalSubmultiples
Indicates that one quantity represents a standard or commonly used fractional multiple of another quantity (e.g., milli-, micro-, kilo- as typical submultiples).
- 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_69d8dcfec90481909e926be9767e5779 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d3e7b87c81909dc29defb33c8e00 |
completed | April 20, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69e4a2efec5c8190840704016bf547a1 |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, 11:59 a.m.