Triple
T12697510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kibō no Tō |
E303373
|
entity |
| Predicate | numberOfSeatsInHouseOfRepresentativesPeak |
P106399
|
FINISHED |
| Object | 50 |
—
|
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: 50 | Statement: [Kibō no Tō, numberOfSeatsInHouseOfRepresentativesPeak, 50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSeatsInHouseOfRepresentativesPeak Context triple: [Kibō no Tō, numberOfSeatsInHouseOfRepresentativesPeak, 50]
-
A.
numberOfRepresentatives
Indicates the quantity of representatives associated with a given entity or unit.
-
B.
numberOfSeatsInSenate
Indicates the total count of seats allocated in a given senate.
-
C.
numberOfSeatsInHouseOfCommons
Indicates the total count of seats allocated in the House of Commons.
-
D.
numberOfSenates
Indicates the total count of senate bodies associated with or present in a given context or entity.
-
E.
numberOfColoniesRepresented
Indicates the count of distinct colonies that are represented or involved in relation to a given entity or 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_69d7bdef90d48190b46b88270e780946 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d962a32c6481908ddaddae4ea267bf |
completed | April 10, 2026, 8:50 p.m. |
| PD | Predicate disambiguation | batch_69d960be63f081908a5ef5ef17a311bf |
completed | April 10, 2026, 8:42 p.m. |
| PDg | Predicate description generation | batch_69d96297b81c819081ad1432dc5f15f4 |
completed | April 10, 2026, 8:50 p.m. |
Created at: April 9, 2026, 5:22 p.m.