Triple
T29651761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black Widow II |
E750150
|
entity |
| Predicate | hasStealthCharacteristics |
P194922
|
FINISHED |
| Object | reduced radar cross-section |
—
|
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: reduced radar cross-section | Statement: [Black Widow II, hasStealthCharacteristics, reduced radar cross-section]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStealthCharacteristics Context triple: [Black Widow II, hasStealthCharacteristics, reduced radar cross-section]
-
A.
hasStealthGameplay
Indicates that the subject involves or supports gameplay mechanics centered around stealth, such as avoiding detection or silently bypassing opponents.
-
B.
isStealthUnit
Indicates that an entity functions as a unit specialized in remaining hidden or undetected from opponents.
-
C.
hasThiefCharacter
Indicates that an entity includes or features a character whose role or identity is that of a thief.
-
D.
hasCloaking
chosen
Indicates that an entity possesses or is equipped with a cloaking capability that can conceal or obscure it.
-
E.
hasHumanCharacteristic
Indicates that an entity possesses a trait, quality, or behavior typically associated with humans.
- 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_69f0d6226fe881908819197c9ef9ee04 |
completed | April 28, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69fdd2be648c8190b60b3d1caeb44364 |
completed | May 8, 2026, 12:10 p.m. |
| PD | Predicate disambiguation | batch_69fdd14a5c708190a6f95ec61f4fc28f |
completed | May 8, 2026, 12:04 p.m. |
Created at: April 28, 2026, 6:52 p.m.