Triple
T296859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Door |
E6110
|
entity |
| Predicate | hasAccord |
P10885
|
FINISHED |
| Object | floral accord |
—
|
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: floral accord | Statement: [Red Door, hasAccord, floral accord]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAccord Context triple: [Red Door, hasAccord, floral accord]
-
A.
hasPar
Indicates a relationship where one entity has another entity as its parent.
-
B.
hasSign
Indicates that an entity possesses, displays, or is associated with a particular sign or symbol.
-
C.
basedOnAgreementWith
Indicates that something exists, is done, or is determined on the basis of a prior agreement or accord between the involved parties.
-
D.
canEnterIntoAgreementsWith
Indicates that one entity has the legal or formal capacity to make binding agreements or contracts with another entity.
-
E.
hasCase
Indicates that one entity is involved in, associated with, or characterized by a particular case, instance, or occurrence represented by another entity.
- 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea4778cc8190be7b648a82542891 |
completed | Feb. 28, 2026, 1:14 p.m. |
| PD | Predicate disambiguation | batch_69a2e937af888190a0960708f09ae033 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea4545608190898436c72e10f39d |
completed | Feb. 28, 2026, 1:14 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.