Triple
T26373454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Synapsida |
E660829
|
entity |
| Predicate | temporalFenestraCountPerSide |
P160467
|
FINISHED |
| Object | one |
—
|
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: one | Statement: [Synapsida, temporalFenestraCountPerSide, one]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temporalFenestraCountPerSide Context triple: [Synapsida, temporalFenestraCountPerSide, one]
-
A.
numberOfColumnsOnFlanks
Indicates the count of columns located on the flanking sides of a structure or object.
-
B.
numberOfTimeSlotsPerFrame
Indicates the total count of discrete time slots that are contained within a single frame in a time-structured system or protocol.
-
C.
numberOfColumnsPerShortSide
Indicates the count of columns that appear along each of the shorter sides of a rectangular or similarly shaped structure or layout.
-
D.
oversPerSide
Indicates the number of overs allocated to each side (team or player) in a match or contest.
-
E.
numberOfFieldPeriods
Indicates the total count of distinct field periods associated with or occurring within a given context or 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_69ee812a698881908d6a58265995fa39 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f610310d7c8190a14a7f7aa377846a |
completed | May 2, 2026, 2:54 p.m. |
| PD | Predicate disambiguation | batch_69f5f800fa9c8190aab0962669fde8ac |
completed | May 2, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69f6018ceb1c8190a6a5f84071659a96 |
completed | May 2, 2026, 1:52 p.m. |
Created at: April 26, 2026, 10:59 p.m.