Triple
T1144101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DAX |
E23523
|
entity |
| Predicate | previousNumberOfConstituents |
P24416
|
FINISHED |
| Object | 30 |
—
|
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: 30 | Statement: [DAX, previousNumberOfConstituents, 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: previousNumberOfConstituents Context triple: [DAX, previousNumberOfConstituents, 30]
-
A.
numberOfConstituents
Indicates the total count of individual components or members that make up a larger whole or group.
-
B.
hasNumberOfConstituencies
Indicates the specific count of constituencies associated with an entity.
-
C.
numberOfColoniesRepresented
Indicates the count of distinct colonies that are represented or involved in relation to a given entity or context.
-
D.
numberOfRepresentatives
Indicates the quantity of representatives associated with a given entity or unit.
-
E.
firstOfficeHoldersCount
Indicates the number of individuals who initially held a particular office or position.
- 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_69a493ef399c8190b04b9146d2314f59 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc5008d8819095c1ffb5db5b4911 |
completed | March 1, 2026, 10:23 p.m. |
| PD | Predicate disambiguation | batch_69a4bb4d4104819084027a043c6118cb |
completed | March 1, 2026, 10:18 p.m. |
| PDg | Predicate description generation | batch_69a4bbb9fb4c81909dd39c496893c21b |
completed | March 1, 2026, 10:20 p.m. |
Created at: March 1, 2026, 7:44 p.m.