Triple
T37919977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Bleu de Gascogne |
E945925
|
entity |
| Predicate | coatMaintenance |
P42008
|
FINISHED |
| Object | low to moderate grooming needs |
—
|
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: low to moderate grooming needs | Statement: [Grand Bleu de Gascogne, coatMaintenance, low to moderate grooming needs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coatMaintenance Context triple: [Grand Bleu de Gascogne, coatMaintenance, low to moderate grooming needs]
-
A.
maintenanceType
Indicates the specific category or kind of maintenance activity associated with an entity or relationship.
-
B.
coatAdaptation
Indicates that an entity’s coat or outer covering has changed or developed in response to environmental or functional conditions.
-
C.
maintenancePractice
chosen
Indicates the specific actions or methods used to preserve, repair, or optimize the condition or performance of something over time.
-
D.
typeOfWear
Indicates the specific manner or style in which something is worn or used as clothing or adornment.
-
E.
repairs
Indicates that one entity fixes, restores, or maintains another entity to a proper or functional condition.
- 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_69f76ef2ebd88190be5229f2621070b3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbc7b78f9481909f4f8fc2e3fdcde1 |
completed | May 6, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69fbbd18c9908190928d274f8731dfa8 |
completed | May 6, 2026, 10:13 p.m. |
Created at: May 3, 2026, 4:20 p.m.