Triple
T8225329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sustainable Development Goal 3 |
E192160
|
entity |
| Predicate | hasTargetCount |
P9099
|
FINISHED |
| Object | 13 |
—
|
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: 13 | Statement: [Sustainable Development Goal 3, hasTargetCount, 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTargetCount Context triple: [Sustainable Development Goal 3, hasTargetCount, 13]
-
A.
hasTarget
Indicates that one entity is directed toward, aimed at, or intended to affect another specific entity as its target.
-
B.
numberOfTargets
chosen
Indicates the quantity of target entities associated with or affected by a given subject or event.
-
C.
hasComponentCount
Indicates that an entity is associated with a specific number of components it contains or comprises.
-
D.
hasFixedCount
Indicates that something is associated with a specific, unchanging number or quantity.
-
E.
hasTotalNumber
Indicates that an entity is associated with a specific overall count or sum of items, elements, or units.
- 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_69ca82c9a8ac81908b011c38698456e4 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb77cdcc248190bc6c5b0271da8a08 |
completed | March 31, 2026, 7:29 a.m. |
| PD | Predicate disambiguation | batch_69cb36af41e081909dee92b9bc4947f1 |
completed | March 31, 2026, 2:51 a.m. |
Created at: March 30, 2026, 5:45 p.m.