Triple
T3428507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TAG Heuer Carrera |
E72281
|
entity |
| Predicate | waterResistance |
P17203
|
FINISHED |
| Object | varies by model |
—
|
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: varies by model | Statement: [TAG Heuer Carrera, waterResistance, varies by model]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: waterResistance Context triple: [TAG Heuer Carrera, waterResistance, varies by model]
-
A.
waterResistanceRating
chosen
Indicates the level to which something can resist water penetration or damage under specified conditions.
-
B.
designedToWithstand
Indicates that something has been intentionally created or engineered to resist, endure, or remain functional under specified conditions, forces, or stresses.
-
C.
wetnessLevel
Indicates the degree or intensity of how wet something is in relation to a reference state or scale.
-
D.
hasWaterDepthCategory
Indicates the classification of something based on the range or category of its water depth.
-
E.
hasWaterActivity
Indicates that one entity possesses, exhibits, or is characterized by a particular level or type of water-related activity (such as moisture content, water availability, or water-based processes).
- 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_69ad85ae14308190bcbc25cfa0246c0b |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb983f4608190abcc27aa7b926deb |
completed | March 8, 2026, 6:01 p.m. |
| PD | Predicate disambiguation | batch_69adadfea024819094b41a13bc004bda |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:15 p.m.