Triple
T24270056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | S&P 500 Value |
E605251
|
entity |
| Predicate | targetExposure |
P860
|
FINISHED |
| Object | value factor |
—
|
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: value factor | Statement: [S&P 500 Value, targetExposure, value factor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetExposure Context triple: [S&P 500 Value, targetExposure, value factor]
-
A.
exposureType
Indicates the specific manner or context in which one entity is exposed to another entity, condition, or influence.
-
B.
exposureLevel
Indicates the degree or intensity to which an entity is subjected or exposed to a particular factor, condition, or influence.
-
C.
regionExposure
Indicates that an entity is subject to or affected by exposure within a specific geographic or spatial region.
-
D.
target
chosen
Indicates that one entity is the intended object, goal, or focus of another entity’s action or attention.
-
E.
exposureModes
Indicates the different ways or conditions under which an entity can be exposed to another entity, factor, or influence.
- 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_69e2954707dc8190915551eb114cfff6 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f28d58666881909f28f4d4f0f7d590 |
completed | April 29, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69f1c450aa508190bc9d372a5f6ee47a |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 18, 2026, 12:07 a.m.