Triple
T38226892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lieutenant colonel (United States Army) |
E1012168
|
entity |
| Predicate | typicalUnitSizeCommanded |
P3664
|
FINISHED |
| Object | 300 to 1,000 soldiers |
—
|
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: 300 to 1,000 soldiers | Statement: [Lieutenant colonel (United States Army), typicalUnitSizeCommanded, 300 to 1,000 soldiers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalUnitSizeCommanded Context triple: [Lieutenant colonel (United States Army), typicalUnitSizeCommanded, 300 to 1,000 soldiers]
-
A.
typicalUnitSize
chosen
Indicates the standard or most common size or quantity in which something is typically measured, packaged, or used.
-
B.
typeOfUnitCommanded
Indicates the specific type or category of military or organizational unit that an entity is in command of.
-
C.
minimumUnitSize
Indicates that there is a smallest allowable or defined size or quantity for the unit involved in the relationship.
-
D.
typicalPackSize
Indicates the usual quantity of items contained together in a single package for that entity.
-
E.
typicalUnitType
Indicates that one entity is the standard or commonly used unit type associated with measuring or expressing the other entity.
- 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_69f76dd25e0c81909f2abd0803e5e3ee |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1c850c819088795a7ae59bdeb8 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:30 p.m.