Triple
T26007877
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | All American patch of the 82nd Airborne Division |
E646809
|
entity |
| Predicate | hasLetterColor |
P168045
|
FINISHED |
| Object | white |
—
|
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: white | Statement: [All American patch of the 82nd Airborne Division, hasLetterColor, white]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLetterColor Context triple: [All American patch of the 82nd Airborne Division, hasLetterColor, white]
-
A.
hasCapColor
Indicates that an entity possesses a cap whose color is specified by the related value.
-
B.
hoodColor
Indicates the color attribute specifically of an entity’s hood (e.g., the hood of a garment or vehicle).
-
C.
hasLetter
Indicates that one entity contains, includes, or is associated with a specific letter or character.
-
D.
hasLetterType
Indicates that an entity is associated with or classified by a specific type or category of letter.
-
E.
hasLetterBy
Indicates that an entity possesses or is associated with a letter authored or sent by another entity.
- 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_69e77e89d5848190b54352cdb74f6029 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f673633d288190b52ceb9f8a057c44 |
completed | May 2, 2026, 9:57 p.m. |
| PD | Predicate disambiguation | batch_69f66ec3d3d48190ab2f2b71939e572e |
completed | May 2, 2026, 9:38 p.m. |
| PDg | Predicate description generation | batch_69f67256d064819094be04fc1bbbc635 |
completed | May 2, 2026, 9:53 p.m. |
Created at: April 22, 2026, 9:01 a.m.