Triple
T913452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Estates-General of 1789 |
E19715
|
entity |
| Predicate | numberOfDeputiesThirdEstate |
P10538
|
FINISHED |
| Object | about 600 |
—
|
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 600 | Statement: [Estates-General of 1789, numberOfDeputiesThirdEstate, about 600]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDeputiesThirdEstate Context triple: [Estates-General of 1789, numberOfDeputiesThirdEstate, about 600]
-
A.
numberOfColoniesRepresented
Indicates the count of distinct colonies that are represented or involved in relation to a given entity or context.
-
B.
orderOfDeputies
Indicates the hierarchical sequence or ranking of deputies within a governing or organizational structure.
-
C.
numberOfDelegates
chosen
Indicates the quantity of delegates associated with or assigned to a particular entity or event.
-
D.
numberOfRepresentatives
Indicates the quantity of representatives associated with a given entity or unit.
-
E.
numberOfElectors
Indicates the total count of electors associated with a given entity or context.
- 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_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. |
Created at: March 1, 2026, 7:39 p.m.