Triple
T27839202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | African spoonbill |
E703633
|
entity |
| Predicate | hasBareSkinColor |
P137296
|
FINISHED |
| Object | red facial skin during breeding season |
—
|
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: red facial skin during breeding season | Statement: [African spoonbill, hasBareSkinColor, red facial skin during breeding season]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBareSkinColor Context triple: [African spoonbill, hasBareSkinColor, red facial skin during breeding season]
-
A.
hasFacialSkinColor
chosen
Indicates that one entity has a specific facial skin color characterized or attributed by another entity.
-
B.
hasSkin
Indicates that one entity possesses skin as a covering or outer tissue layer.
-
C.
hasFleshColor
Indicates that an entity possesses a particular color or characteristic of its flesh or internal tissue.
-
D.
skinColorDescription
Indicates a descriptive characterization of an entity’s skin color.
-
E.
fruitSkinColor
Indicates the color of the outer skin or peel of a fruit.
- 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_69ef840d9e3c819093615ebff4ec22be |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69f6b2a65c7c8190ac40f1466ceadefc |
completed | May 3, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f6b14d7d508190bc7d4c89dfba4a32 |
completed | May 3, 2026, 2:22 a.m. |
Created at: April 27, 2026, 6:01 p.m.