Triple
T33608636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1988 United States Senate elections |
E860925
|
entity |
| Predicate | beforeParty1Seats |
P176954
|
FINISHED |
| Object | 55 |
—
|
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: 55 | Statement: [1988 United States Senate elections, beforeParty1Seats, 55]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: beforeParty1Seats Context triple: [1988 United States Senate elections, beforeParty1Seats, 55]
-
A.
party1Seats
Indicates the number of seats held or allocated to the first party in a multi-party context.
-
B.
seatsForParty
Indicates that a seating arrangement or capacity is designated to accommodate a specific party or group.
-
C.
party2Seats
Indicates the number of seats held or allocated to the second party in a given context (such as an election or governing body).
-
D.
beforePartyInGovernment
Indicates that one political party held power in government earlier in time than another party.
-
E.
party6Seats
Indicates the number of seats held or allocated to a sixth party in a multi-party context.
- 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_69f3498037c88190a4500f002b5540e0 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f8164698819090c1b471f1caa4c6 |
completed | May 3, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69f6f6632dfc8190af85e258c8519207 |
completed | May 3, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69f6f70b0ca081908b24a98937e6ef66 |
completed | May 3, 2026, 7:19 a.m. |
Created at: May 1, 2026, 1:41 a.m.