Triple
T2779375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quantitative Reasoning |
E61654
|
entity |
| Predicate | includesContentArea |
P6142
|
FINISHED |
| Object | word problems |
—
|
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: word problems | Statement: [Quantitative Reasoning, includesContentArea, word problems]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesContentArea Context triple: [Quantitative Reasoning, includesContentArea, word problems]
-
A.
containsThemeArea
chosen
Indicates that one entity includes or encompasses a specific thematic area as part of its scope or content.
-
B.
includesAreaType
Indicates that one entity encompasses or contains another entity of a specified area type within its scope or boundaries.
-
C.
includesAreasOff
Indicates that something encompasses or covers areas that are turned off, inactive, or excluded from normal operation.
-
D.
textsIncludedIn
Indicates that certain texts are contained within, or form a subset of, a larger collection or body of texts.
-
E.
areaComponent
Indicates that one area is a constituent or sub-area that forms part of a larger area.
- 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_69ab4b7e43c48190997b8fc8fb1663ab |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddceb9d88190961e30d521a21552 |
completed | March 7, 2026, 8:11 a.m. |
| PD | Predicate disambiguation | batch_69abdd00b65c8190a8ea444308c4fa2b |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:57 p.m.