Triple
T14325953
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MBTA Red Line 1500/1600 series cars |
E355214
|
entity |
| Predicate | hasFleetNumberSeries |
P44071
|
FINISHED |
| Object | 1500 series |
—
|
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: 1500 series | Statement: [MBTA Red Line 1500/1600 series cars, hasFleetNumberSeries, 1500 series]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFleetNumberSeries Context triple: [MBTA Red Line 1500/1600 series cars, hasFleetNumberSeries, 1500 series]
-
A.
hasSeriesNumber
Indicates that an entity is assigned a specific ordinal or sequence number within a series or ordered set.
-
B.
usesFleetOf
Indicates that one entity operates or relies on a group of vehicles, vessels, or similar assets collectively as a fleet to perform its activities or services.
-
C.
hasModelSeries
Indicates a relationship where an item or product is associated with a specific model series it belongs to.
-
D.
belongsToFleet
Indicates that an entity is a member of, or is assigned to, a specific fleet or group of vehicles/vessels.
-
E.
fleetNumbers
chosen
Indicates that there is an association between an entity and one or more identifying numbers assigned to it as part of a fleet.
- 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_69d8278fa2108190bc0d0e7939c1eb03 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de883e6a288190b6c22f630a1eef3c |
completed | April 14, 2026, 6:32 p.m. |
| PD | Predicate disambiguation | batch_69de2a9515f4819081aabf251bca5878 |
completed | April 14, 2026, 11:52 a.m. |
Created at: April 10, 2026, 1:13 a.m.