Triple
T269624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scorpion W2 |
E5601
|
entity |
| Predicate | patternFeatures |
P5084
|
FINISHED |
| Object | macro and micro pattern elements |
—
|
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: macro and micro pattern elements | Statement: [Scorpion W2, patternFeatures, macro and micro pattern elements]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: patternFeatures Context triple: [Scorpion W2, patternFeatures, macro and micro pattern elements]
-
A.
kitPattern
Indicates the design or visual pattern featured on a team's kit or uniform.
-
B.
notationPattern
Indicates a recurring way in which something is symbolically represented or written, such as a consistent style or structure of notation used for an entity or concept.
-
C.
typicalFeatures
chosen
Indicates that the related entities are characteristic or commonly occurring features or attributes of something.
-
D.
featuresText
Indicates that an entity includes or presents a specific piece of text as one of its characteristics or contents.
-
E.
linguisticFeature
Indicates a relationship where a linguistic property, pattern, or characteristic is attributed to or associated with a language-related entity (such as a word, phrase, or text).
- 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_69a25853594c8190b05ec3a586ec88bf |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25db00ed48190be6f598807265356 |
completed | Feb. 28, 2026, 3:14 a.m. |
| PD | Predicate disambiguation | batch_69a25b70d99c819085d8381a313a2a34 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:57 a.m.