Triple
T3249446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fécondité |
E68139
|
entity |
| Predicate | hasIntendedEffect |
P46826
|
FINISHED |
| Object | influence social policy debates on population |
—
|
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: influence social policy debates on population | Statement: [Fécondité, hasIntendedEffect, influence social policy debates on population]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIntendedEffect Context triple: [Fécondité, hasIntendedEffect, influence social policy debates on population]
-
A.
hasConsequence
Indicates that one event, action, or condition leads to or results in another as its outcome or effect.
-
B.
hasLegalEffect
Indicates that an action, document, or condition produces recognized legal consequences or enforceable rights and obligations.
-
C.
hasCommonSideEffect
Indicates that two or more treatments, drugs, or interventions share at least one side effect in common.
-
D.
possibleSideEffect
Indicates that one entity may occur as a side effect or unintended consequence of another entity or action.
-
E.
intendedFunction
Indicates that something is designed or purposed to perform a particular role, use, or function.
- 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_69ad858e4c708190aa31d486cfee8a6a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaf3fc3c8819080ac95974581ca0e |
completed | March 8, 2026, 5:17 p.m. |
| PD | Predicate disambiguation | batch_69ada41837e48190933572165be0ca38 |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69ada525bb2c8190b773efe6d696b6ab |
completed | March 8, 2026, 4:34 p.m. |
Created at: March 8, 2026, 3:09 p.m.