Triple
T5178979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Delaware General Assembly |
E116868
|
entity |
| Predicate | numberOfMembersInLowerHouse |
P45157
|
FINISHED |
| Object | 41 |
—
|
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: 41 | Statement: [Delaware General Assembly, numberOfMembersInLowerHouse, 41]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMembersInLowerHouse Context triple: [Delaware General Assembly, numberOfMembersInLowerHouse, 41]
-
A.
numberOfMembersUpperHouse
Indicates the total count of individuals serving as members in the upper house of a legislative body.
-
B.
lowerHouseComposition
Indicates the makeup or distribution of seats among parties or groups within the lower house of a legislature.
-
C.
hasLowerHouseSeats
Indicates the number of seats an entity holds in the lower house of a bicameral legislature.
-
D.
lowerHouseComposedOf
Indicates that a lower legislative house is made up of, or constituted by, specific members, parties, or organizational units.
-
E.
numberOfMembersInHouse
chosen
Indicates the total count of members that belong to a particular house.
- 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_69bd446140f08190becb93c61158f27f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd79978a208190b2e5909795108327 |
completed | March 20, 2026, 4:45 p.m. |
| PD | Predicate disambiguation | batch_69bd77b529948190b86671ebe43f4734 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:45 p.m.