Triple
T30663382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Case Study Houses |
E780591
|
entity |
| Predicate | numberOfProjects |
P202523
|
FINISHED |
| Object | about 36 |
—
|
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: about 36 | Statement: [Case Study Houses, numberOfProjects, about 36]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfProjects Context triple: [Case Study Houses, numberOfProjects, about 36]
-
A.
numberOfMajorProjects
Indicates the count of significant or primary projects associated with an entity.
-
B.
numberOfProjectsFunded
Indicates the quantity of projects that have received funding from a given source or entity.
-
C.
projectNumber
Indicates a relationship where a specific identifying number is assigned to or associated with a particular project.
-
D.
basedOnManyProjects
Indicates that something is derived from, influenced by, or constructed using multiple underlying projects.
-
E.
hasPlannedProjects
Indicates that an entity is associated with projects that are intended or scheduled to be carried out in the future.
- 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_69f224a6d10481909290be1a00fc83b3 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a008d6085508190a71c52cd028a6297 |
completed | May 10, 2026, 1:51 p.m. |
| PD | Predicate disambiguation | batch_6a008ced31448190b8fc60bf87b40647 |
completed | May 10, 2026, 1:49 p.m. |
| PDg | Predicate description generation | batch_6a008d5fe63c8190ab4f5a31c249c674 |
completed | May 10, 2026, 1:51 p.m. |
Created at: April 29, 2026, 8:31 p.m.