Triple
T3499686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DKNY (licensed watches) |
E73933
|
entity |
| Predicate | watchType |
P48522
|
FINISHED |
| Object | analog watches |
—
|
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: analog watches | Statement: [DKNY (licensed watches), watchType, analog watches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: watchType Context triple: [DKNY (licensed watches), watchType, analog watches]
-
A.
observationType
Indicates the specific kind or category of observation being made or recorded in a given context.
-
B.
behaviorObserved
Indicates that a particular behavior or action has been witnessed, recorded, or detected in relation to an entity or context.
-
C.
viewType
Indicates the specific manner or format in which something is displayed, presented, or visually arranged.
-
D.
sightType
Indicates the specific kind or category of sight or visual perception associated with an entity or event.
-
E.
monitoredFor
Indicates that one entity is being observed or tracked over time to detect, assess, or manage the occurrence or progression of another entity (such as a condition, event, or risk).
- 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_69ad85cdb6e48190a335d412b9194ed8 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbd4eb308190b84e84261ceec229 |
completed | March 8, 2026, 6:11 p.m. |
| PD | Predicate disambiguation | batch_69adae0cd8b0819099da300af09880da |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adaef1037c819082c7af949ec85360 |
completed | March 8, 2026, 5:16 p.m. |
Created at: March 8, 2026, 3:18 p.m.