Triple
T13819993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Job’s Daughters International |
E332109
|
entity |
| Predicate | hasProgramGoal |
P111636
|
FINISHED |
| Object | character building |
—
|
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: character building | Statement: [Job’s Daughters International, hasProgramGoal, character building]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProgramGoal Context triple: [Job’s Daughters International, hasProgramGoal, character building]
-
A.
hasPlanningGoal
Indicates that an entity is associated with, or directed toward achieving, a specific planning objective or target state.
-
B.
hasPrimaryGoal
Indicates that an entity’s main or most important objective is the specified goal.
-
C.
hasOperationalGoal
Indicates that an entity is associated with a specific objective or target it aims to achieve through its operations or activities.
-
D.
hasManagementGoal
Indicates that an entity is associated with a specific management objective or target it is intended to achieve or support.
-
E.
hasCurriculumGoal
Indicates that an educational curriculum includes or is associated with a specific intended learning goal or objective.
- 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_69d81c59f8808190a851bc56afdc55e9 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0282d4d08190b754cda7683408c4 |
completed | April 14, 2026, 9:01 a.m. |
| PD | Predicate disambiguation | batch_69dbc862e9608190bd8a3d883959b7e4 |
completed | April 12, 2026, 4:29 p.m. |
| PDg | Predicate description generation | batch_69dcad0eea9881908f71e1eed9a2446b |
completed | April 13, 2026, 8:45 a.m. |
Created at: April 9, 2026, 10:12 p.m.