Triple
T26708848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lalage leucopyga complex |
E673353
|
entity |
| Predicate | hasBloodTemperature |
P100818
|
FINISHED |
| Object | warm-blooded |
—
|
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: warm-blooded | Statement: [Lalage leucopyga complex, hasBloodTemperature, warm-blooded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBloodTemperature Context triple: [Lalage leucopyga complex, hasBloodTemperature, warm-blooded]
-
A.
hasTemperature
Indicates that an entity possesses or is characterized by a specific temperature value.
-
B.
coreTemperature
Indicates that an entity has a specific internal or central temperature value.
-
C.
hasTemperatureCategory
chosen
Indicates that an entity is associated with a specific qualitative temperature classification (e.g., hot, cold, warm).
-
D.
hasTemperatureRegime
Indicates that an entity is characterized by or associated with a particular pattern or regime of temperature conditions.
-
E.
hasBlood
Indicates that one entity possesses or contains the blood of another entity.
- 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_69eecda3a22881908f3061c760b9d542 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f617bcfbb0819092a1334dc7553a97 |
completed | May 2, 2026, 3:26 p.m. |
| PD | Predicate disambiguation | batch_69f60b8dfa0c8190864e1a940024d0a0 |
completed | May 2, 2026, 2:34 p.m. |
Created at: April 27, 2026, 3:35 a.m.