Triple
T32131833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alvin Murphy |
E820670
|
entity |
| Predicate | goalOfOthers |
P6636
|
FINISHED |
| Object | escort him to a CDC lab in California |
—
|
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: escort him to a CDC lab in California | Statement: [Alvin Murphy, goalOfOthers, escort him to a CDC lab in California]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goalOfOthers Context triple: [Alvin Murphy, goalOfOthers, escort him to a CDC lab in California]
-
A.
aimOfOtherSide
Indicates that one party’s goal or intended outcome is directed toward or defined in relation to the opposing or counterpart side in a relationship or interaction.
-
B.
goalIn
Indicates that one entity’s objective, aim, or intended outcome is located within, directed toward, or achieved inside another entity or context.
-
C.
goals
chosen
Indicates that an entity has objectives, targets, or desired outcomes it aims to achieve.
-
D.
goalsFor
Indicates the number of goals scored by one participant or team in favor of a particular side or match context.
-
E.
goalType
Indicates the specific category or nature of a goal associated with an entity or action.
- F. None of above.
Provenance (3 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_69f349039e0c819091c7a7d322e3f46d |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b97009cc819093326ec5c6a56083 |
completed | May 3, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a970b0819090c6473844ffa8e3 |
completed | May 3, 2026, 2:32 a.m. |
Created at: May 1, 2026, 12:29 a.m.