Triple
T32116894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AXA Investment Managers – Real Assets |
E820261
|
entity |
| Predicate | targetInvestorSegment |
P146874
|
FINISHED |
| Object | pension funds |
—
|
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: pension funds | Statement: [AXA Investment Managers – Real Assets, targetInvestorSegment, pension funds]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetInvestorSegment Context triple: [AXA Investment Managers – Real Assets, targetInvestorSegment, pension funds]
-
A.
targetInvestorNeed
Indicates that an action, product, or communication is specifically aimed at addressing or fulfilling an investor’s particular needs or requirements.
-
B.
investorTargetGroup
chosen
Indicates that a particular group or segment is the intended or primary focus of investment efforts or strategies.
-
C.
targetInvestorRegion
Indicates the geographic region or market area that an investor is focused on or intended to invest in.
-
D.
typeOfInvestor
Indicates the kind or category of investor that a given entity is classified as.
-
E.
targetMarket
Indicates the group of consumers or organizations that a product, service, or campaign is specifically intended and designed to reach.
- 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_69f3490209c881908ec0241476715f15 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fddac4e2f48190a9301d3422658b29 |
completed | May 8, 2026, 12:44 p.m. |
| PD | Predicate disambiguation | batch_69fdda06969c8190b5d033964ea2a690 |
completed | May 8, 2026, 12:41 p.m. |
Created at: May 1, 2026, 12:28 a.m.