Triple
T11967401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. federal greenbelt towns |
E284823
|
entity |
| Predicate | numberOfMajorProjects |
P102552
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [U.S. federal greenbelt towns, numberOfMajorProjects, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMajorProjects Context triple: [U.S. federal greenbelt towns, numberOfMajorProjects, 3]
-
A.
numberOfMajorRevisions
Indicates the count of significant revision events that have occurred for an entity.
-
B.
numberPlanned
Indicates that a specific quantity has been scheduled or intended for a future action or allocation.
-
C.
majorProject
Indicates that an entity is a primary, large-scale, or most significant project associated with another entity.
-
D.
numberOfMainEvents
Indicates the total count of primary or most significant events associated with a given entity or context.
-
E.
projectNumber
Indicates a relationship where a specific identifying number is assigned to or associated with a particular project.
- 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_69d6ab2eaeb881909f7914758f859413 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9037adf5881908abe1a4e64a71f20 |
completed | April 10, 2026, 2:04 p.m. |
| PD | Predicate disambiguation | batch_69d8bb40f30c8190a0e0719bd67542bf |
completed | April 10, 2026, 8:56 a.m. |
| PDg | Predicate description generation | batch_69d8dd0ba0f88190b7d5e358c27ca184 |
completed | April 10, 2026, 11:20 a.m. |
Created at: April 8, 2026, 9:46 p.m.