Triple
T5527225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monument to the Third International |
E144948
|
entity |
| Predicate | functionOfLowestVolume |
P65255
|
FINISHED |
| Object | legislative body meeting space |
—
|
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: legislative body meeting space | Statement: [Monument to the Third International, functionOfLowestVolume, legislative body meeting space]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: functionOfLowestVolume Context triple: [Monument to the Third International, functionOfLowestVolume, legislative body meeting space]
-
A.
lowestPoint
Indicates that one entity is the point with the minimum vertical position or value relative to another entity or within a specified context.
-
B.
lowestRank
Indicates that the subject has the least or worst rank in an ordered set compared to all other related entities.
-
C.
lowestScore
Indicates that the associated value is the smallest (minimum) score among a set of scores.
-
D.
minimumNumber
Indicates that the associated value is the smallest or least quantity allowed, required, or observed within a given set or context.
-
E.
lowestGrade
Indicates that one entity has the smallest or worst grade value compared to all other relevant entities in a given 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_69c008f873a481909b4d9f7e2db3c37d |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f8a34a48190bcbd0036f79246a9 |
completed | March 22, 2026, 4:57 p.m. |
| PD | Predicate disambiguation | batch_69c01b0a06348190b39ac9fe80d2836a |
completed | March 22, 2026, 4:38 p.m. |
| PDg | Predicate description generation | batch_69c01f051e508190b3886d87b4afdd0b |
completed | March 22, 2026, 4:55 p.m. |
Created at: March 22, 2026, 3:34 p.m.