Triple
T37783230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loopy Landscapes |
E941883
|
entity |
| Predicate | numberOfScenariosAdded |
P204384
|
FINISHED |
| Object | over 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: over 30 | Statement: [Loopy Landscapes, numberOfScenariosAdded, over 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfScenariosAdded Context triple: [Loopy Landscapes, numberOfScenariosAdded, over 30]
-
A.
numberOfScenes
Indicates the total count of distinct scenes associated with or contained within an entity.
-
B.
plannedNumberOfTests
Indicates the total count of tests that are intended or scheduled to be conducted for a given context or period.
-
C.
numberOfRulesPlanned
Indicates the planned or intended count of rules associated with an entity or process.
-
D.
numberOfConfigurations
Indicates the total count of distinct configurations associated with or applicable to a given entity or situation.
-
E.
numberOfCases
Indicates the total count of individual instances, occurrences, or records associated with a particular situation, condition, or category.
- 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_69f76ee5cb0c81909a363d1c929156c0 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a1772e48190ba738c6d11b321e2 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:19 p.m.