Triple
T4564950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Python bivittatus |
E121885
|
entity |
| Predicate | maximumRecordedMass |
P57708
|
FINISHED |
| Object | over 90 kilograms |
—
|
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: over 90 kilograms | Statement: [Python bivittatus, maximumRecordedMass, over 90 kilograms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumRecordedMass Context triple: [Python bivittatus, maximumRecordedMass, over 90 kilograms]
-
A.
maximumMaleBodyMass
Indicates the greatest recorded body mass among male individuals within a given group or species.
-
B.
maximumRecordedLifespan
Indicates the greatest length of time that has ever been recorded for an individual of a given type to live.
-
C.
maximumRecordedLength
Indicates the greatest length value that has been observed and recorded for the entity in question.
-
D.
maximumRecordedTemperature
Indicates the highest temperature value that has been observed and recorded for a given entity or context.
-
E.
hasMass_kg
Indicates that an entity possesses a specific mass measured in kilograms.
- 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_69bd463f156881908a99aca69c5721ac |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd589b439c81908da9d19433310bcd |
completed | March 20, 2026, 2:24 p.m. |
| PD | Predicate disambiguation | batch_69bd52254c648190a5144cfe8fa7e409 |
completed | March 20, 2026, 1:56 p.m. |
| PDg | Predicate description generation | batch_69bd56f6e75481909c487a94a2c2d0ba |
completed | March 20, 2026, 2:17 p.m. |
Created at: March 20, 2026, 1:09 p.m.