Triple
T965741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Thatcher government |
E20832
|
entity |
| Predicate | termCount |
P9908
|
FINISHED |
| Object | three consecutive terms |
—
|
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: three consecutive terms | Statement: [Margaret Thatcher government, termCount, three consecutive terms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: termCount Context triple: [Margaret Thatcher government, termCount, three consecutive terms]
-
A.
titleCount
Indicates the number of distinct titles associated with an entity within a given context.
-
B.
hasNumberOfTerms
chosen
Indicates the quantity of distinct terms or elements associated with a given entity or expression.
-
C.
branchCount
Indicates the number of branches associated with a given entity or structure.
-
D.
wordCount
Indicates the total number of words contained in a given text or linguistic unit.
-
E.
parameterCount
Indicates the number of parameters associated with a given function, method, or callable 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b431d61481908b53490e99670363 |
completed | March 1, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a42c1481908d940cbe0aefdd3b |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.