Triple
T31671434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sundadanio |
E808278
|
entity |
| Predicate | waterColorAssociation |
P13022
|
FINISHED |
| Object | tea‑colored blackwater |
—
|
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: tea‑colored blackwater | Statement: [Sundadanio, waterColorAssociation, tea‑colored blackwater]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: waterColorAssociation Context triple: [Sundadanio, waterColorAssociation, tea‑colored blackwater]
-
A.
waterColor
Indicates that one entity is the color or hue characteristic of water associated with another entity.
-
B.
hasWaterColor
chosen
Indicates that an entity possesses or is characterized by a particular color of water.
-
C.
hasWaterColorCause
Indicates that one entity is the cause or reason for the particular color or coloration of another entity’s water.
-
D.
waterDepiction
Indicates that one entity visually represents or portrays water in some form (e.g., as a subject, element, or feature) in an image or depiction.
-
E.
hasDistinctWaterColorFor
Indicates that one entity exhibits a water color that is noticeably different or unique compared to another specified entity or context.
- 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_69f348dbeef4819080b446a7feb6340b |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fcf825ca7081909d06b0df33eb33f9 |
completed | May 7, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69fcf42160f0819096812a8bf590875e |
completed | May 7, 2026, 8:20 p.m. |
Created at: April 30, 2026, 11:01 p.m.