Triple
T28754700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 100th Fighter Squadron |
E731635
|
entity |
| Predicate | colorMarkings |
P171798
|
FINISHED |
| Object | red tail markings on aircraft |
—
|
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: red tail markings on aircraft | Statement: [100th Fighter Squadron, colorMarkings, red tail markings on aircraft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: colorMarkings Context triple: [100th Fighter Squadron, colorMarkings, red tail markings on aircraft]
-
A.
coatMarkings
Indicates how an entity’s coat is patterned or marked, such as stripes, spots, or other distinctive visual markings.
-
B.
billMarkings
Indicates a relationship where specific markings or patterns are present on or associated with a bill (such as a beak or financial document).
-
C.
eggMarkings
Indicates that one entity bears or displays specific markings or patterns on its eggs in relation to another entity or context.
-
D.
hasFieldMarkingsFor
Indicates that one entity includes or displays field markings that are intended or suitable for use by another entity.
-
E.
leafMarkings
Indicates the presence, pattern, or characteristics of markings found on the surface of a leaf.
- 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_69f043ed68a881909e858a06bab7a247 |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69f6a28c7c148190bfc980aad9f678ca |
completed | May 3, 2026, 1:19 a.m. |
| PD | Predicate disambiguation | batch_69f69fe1e3c88190830bb2e9f407357e |
completed | May 3, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69f6a28b8ea881908733485374771c51 |
completed | May 3, 2026, 1:19 a.m. |
Created at: April 28, 2026, 6:09 a.m.