Triple
T913454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Estates-General of 1789 |
E19715
|
entity |
| Predicate | numberOfDeputiesSecondEstate |
P22868
|
FINISHED |
| Object | about 300 |
—
|
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: about 300 | Statement: [Estates-General of 1789, numberOfDeputiesSecondEstate, about 300]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDeputiesSecondEstate Context triple: [Estates-General of 1789, numberOfDeputiesSecondEstate, about 300]
-
A.
orderOfDeputies
Indicates the hierarchical sequence or ranking of deputies within a governing or organizational structure.
-
B.
numberOfSenators
Indicates the total count of senators associated with a given political body, region, or entity.
-
C.
hasNumberOfCouncillors
Indicates the relationship that specifies how many councillors are associated with a given entity.
-
D.
numberOfDelegates
Indicates the quantity of delegates associated with or assigned to a particular entity or event.
-
E.
totalNumberOfLegislators
Indicates the total count of legislators associated with a given political body, jurisdiction, or legislative session.
- 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_69a4939f91a08190ba68c2c81eab90fe |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b6755c488190b7f7848110e3ea2c |
completed | March 1, 2026, 9:58 p.m. |
| PD | Predicate disambiguation | batch_69a4b292d3408190947cbc2f794cf8c5 |
completed | March 1, 2026, 9:41 p.m. |
| PDg | Predicate description generation | batch_69a4b67499708190a65f24d1fd7e4ec5 |
completed | March 1, 2026, 9:58 p.m. |
Created at: March 1, 2026, 7:39 p.m.