Triple
T8988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fair Deal |
E179
|
entity |
| Predicate | appliesToDemographic |
P380
|
FINISHED |
| Object | American workers |
—
|
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: American workers | Statement: [Fair Deal, appliesToDemographic, American workers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesToDemographic Context triple: [Fair Deal, appliesToDemographic, American workers]
-
A.
demographics
Indicates the relationship of providing or characterizing statistical information about a population’s attributes, such as age, gender, income, or education.
-
B.
demographicsCharacteristic
Indicates that one entity serves as a demographic attribute or characteristic (such as age, gender, ethnicity, etc.) that describes or classifies another entity.
-
C.
demographicsNote
Indicates that there is an associated note or commentary describing demographic-related information about an entity.
-
D.
eligibility
Indicates that an entity meets the required conditions or qualifications to participate in, receive, or perform something.
-
E.
isAbout
chosen
Indicates that one entity has as its subject, focus, or primary concern the content, topic, or theme represented by another entity.
- 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_69a23bb612708190b09f25385e4b63d1 |
completed | Feb. 28, 2026, 12:49 a.m. |
| NER | Named-entity recognition | batch_69a240b249788190af8dbf7e80e9c91b |
completed | Feb. 28, 2026, 1:11 a.m. |
| PD | Predicate disambiguation | batch_69a23fe52ec48190a4d24101c91434ed |
completed | Feb. 28, 2026, 1:07 a.m. |
Created at: Feb. 28, 2026, 12:54 a.m.