Triple
T1670384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MBTA bus route 39 |
E36109
|
entity |
| Predicate | isHeavilyUsed |
P29905
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [MBTA bus route 39, isHeavilyUsed, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isHeavilyUsed Context triple: [MBTA bus route 39, isHeavilyUsed, true]
-
A.
isHeavilyWeightedToward
Indicates that something is strongly biased or disproportionately oriented in favor of one side, option, or aspect over others.
-
B.
isWidelyUsed
chosen
Indicates that something is commonly or extensively utilized across many contexts, users, or situations.
-
C.
isFrequently
Indicates that an action, state, or relationship occurs often or with high regularity between the related entities.
-
D.
isInUse
Indicates that an entity is currently being utilized or actively engaged in its intended function or operation.
-
E.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
- 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_69a8861286808190939afff3ce8ee31e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69ab272a653481908f48aa1eed5de8a4 |
completed | March 6, 2026, 7:12 p.m. |
| PD | Predicate disambiguation | batch_69aa61b2f6288190b2348ef7d7e4672d |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:29 p.m.