Triple
T63082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Special Forces Branch |
E1251
|
entity |
| Predicate | authorizedHeadgear |
P3666
|
FINISHED |
| Object | green beret |
—
|
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: green beret | Statement: [Special Forces Branch, authorizedHeadgear, green beret]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: authorizedHeadgear Context triple: [Special Forces Branch, authorizedHeadgear, green beret]
-
A.
wears
Indicates that one entity is dressed in, or has on its body, a particular item such as clothing or accessories.
-
B.
hasTypeOfInsignia
Indicates that an entity bears or is associated with a specific kind or category of insignia.
-
C.
recognizedFor
Indicates that one entity is acknowledged, credited, or honored for a particular achievement, quality, contribution, or work associated with another entity.
-
D.
styleGranted
Indicates that a particular style, manner, or mode of expression has been conferred or authorized for use by one entity to another.
-
E.
hatPattern
Indicates that one entity has a hat characterized by a specific pattern or design.
- 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_69a24ba4f760819081f6638a3c70538a |
completed | Feb. 28, 2026, 1:57 a.m. |
| NER | Named-entity recognition | batch_69a24fd16c248190a6ee4cd96c388772 |
completed | Feb. 28, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69a24ea3c44081908fa3856969881d1f |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a24fcf5a88819088c5fa4c08476358 |
completed | Feb. 28, 2026, 2:15 a.m. |
Created at: Feb. 28, 2026, 2:02 a.m.