Triple
T1647209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Project 211 |
E35609
|
entity |
| Predicate | numberOfTargetInstitutions |
P30845
|
FINISHED |
| Object | approximately 100 |
—
|
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: approximately 100 | Statement: [Project 211, numberOfTargetInstitutions, approximately 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTargetInstitutions Context triple: [Project 211, numberOfTargetInstitutions, approximately 100]
-
A.
hasNumberOfMemberInstitutions
Indicates the quantitative count of member institutions associated with a given entity.
-
B.
numberOfMemberOrganizations
Indicates the total count of organizations that are members of a given group, association, or umbrella entity.
-
C.
numberOfSites
Indicates the total count of distinct sites associated with or involved in the given entity or context.
-
D.
numberOfTargets
Indicates the quantity of target entities associated with or affected by a given subject or event.
-
E.
numberOfCampuses
Indicates the total count of campuses associated with a given entity.
- 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_69a8860568888190a32cd9f70acbba42 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaa0fbe984819084f8daee81ca9b67 |
completed | March 6, 2026, 9:40 a.m. |
| PD | Predicate disambiguation | batch_69a907ce4dd881909168a1e99505d4ec |
completed | March 5, 2026, 4:34 a.m. |
| PDg | Predicate description generation | batch_69a949509d508190a3a35554996823de |
completed | March 5, 2026, 9:13 a.m. |
Created at: March 4, 2026, 7:28 p.m.