Triple
T1152827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | McKinney-Vento Homeless Assistance Act |
E23716
|
entity |
| Predicate | primaryTopic |
P12980
|
FINISHED |
| Object | homelessness |
—
|
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: homelessness | Statement: [McKinney-Vento Homeless Assistance Act, primaryTopic, homelessness]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryTopic Context triple: [McKinney-Vento Homeless Assistance Act, primaryTopic, homelessness]
-
A.
primaryTopicOf
chosen
Indicates that a given subject is the main or central topic described by another resource (such as a document, page, or record).
-
B.
primaryContent
Indicates that one entity serves as the main or most important content associated with another entity.
-
C.
primaryTarget
Indicates that an entity is the main or most important target of another entity’s action, focus, or effect.
-
D.
primaryArea
Indicates that one entity is the main or most important area, domain, or field associated with another entity.
-
E.
primaryMode
Indicates the main or most commonly used method, manner, or form in which an action, process, or interaction is carried out between entities.
- 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_69a493f0d32c8190ac74bad3c87f2641 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc8d2dd8819081c779d408c2651d |
completed | March 1, 2026, 10:24 p.m. |
| PD | Predicate disambiguation | batch_69a4bb50d19c81908a98dbbb04a8906f |
completed | March 1, 2026, 10:18 p.m. |
Created at: March 1, 2026, 7:44 p.m.