Triple
T147605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Will |
E3364
|
entity |
| Predicate | oftenPerceivedAs |
P2289
|
FINISHED |
| Object | casual |
—
|
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: casual | Statement: [Will, oftenPerceivedAs, casual]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oftenPerceivedAs Context triple: [Will, oftenPerceivedAs, casual]
-
A.
oftenConfusedWith
chosen
Indicates that one entity is frequently mistaken for or thought to be another due to similarity or ambiguity.
-
B.
characterizedBy
Indicates that one entity possesses a defining quality, feature, or attribute expressed by another entity.
-
C.
oftenAccompaniedBy
Indicates that one entity is frequently found together with, occurs alongside, or is commonly associated in presence or use with another entity.
-
D.
colonialPerception
Indicates how one entity views, interprets, or characterizes another through the lens of colonial power dynamics, assumptions, and hierarchies.
-
E.
discriminatedAgainst
Indicates that one entity treats another unfairly or unequally based on a particular characteristic, such as race, gender, or other protected attributes.
- 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_69a252868de4819080e21c9938bfe8b6 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a258808ff08190a06b6206f635612b |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a256580c2c8190beecca60ca8595f3 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.