Triple
T286002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MURI |
E5885
|
entity |
| Predicate | typicalTeamComposition |
P10366
|
FINISHED |
| Object | multiple principal investigators |
—
|
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: multiple principal investigators | Statement: [MURI, typicalTeamComposition, multiple principal investigators]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTeamComposition Context triple: [MURI, typicalTeamComposition, multiple principal investigators]
-
A.
typicalTeamSize
Indicates the usual or most common number of members that make up a given team.
-
B.
typicalTeamDesignation
Indicates the standard or commonly used team name or label that is typically assigned to an entity.
-
C.
typicalMembers
Indicates that the related entities are representative or characteristic members of a larger group, category, or class.
-
D.
typeOfTeams
Indicates the categories or kinds of teams to which an entity or group of entities belongs.
-
E.
ALTeam
Indicates that one entity is a member of, or associated with, a particular team or group.
- 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_69a25946a7ac8190a78871c210213272 |
completed | Feb. 28, 2026, 2:56 a.m. |
| NER | Named-entity recognition | batch_69a2605b372c8190831570aa6532cc96 |
completed | Feb. 28, 2026, 3:26 a.m. |
| PD | Predicate disambiguation | batch_69a25b7a8d148190aacdcc8ccb35c7f3 |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a2605a3d988190a8872169fd8eb2e8 |
completed | Feb. 28, 2026, 3:26 a.m. |
Created at: Feb. 28, 2026, 3:02 a.m.