Triple
T14467879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MBTA Red Line 1800 series cars |
E358760
|
entity |
| Predicate | hasCarNumberRange |
P67272
|
FINISHED |
| Object | 1800 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: 1800 series | Statement: [MBTA Red Line 1800 series cars, hasCarNumberRange, 1800 series]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCarNumberRange Context triple: [MBTA Red Line 1800 series cars, hasCarNumberRange, 1800 series]
-
A.
typicalCarNumberRange
chosen
Indicates the usual or commonly expected numerical range of cars associated with an entity (such as a location, time period, or context).
-
B.
RVNumberRange
Indicates that a recreational vehicle’s number or identifier falls within a specified numeric range.
-
C.
hasRange
Indicates that a property or relation is constrained to take its values from a specified class, type, or value set.
-
D.
carNumberUsed
Indicates that a specific car number has been used or assigned in a given context or event.
-
E.
hasShipNumberRange
Indicates that an entity is associated with ships whose identification numbers fall within a specified numeric range.
- 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_69d827966698819082e140837737501d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91f8613c819080424104c0b7f4c3 |
completed | April 14, 2026, 7:14 p.m. |
| PD | Predicate disambiguation | batch_69de5c42bd3c81909a62acf30cc24d1e |
completed | April 14, 2026, 3:24 p.m. |
Created at: April 10, 2026, 1:19 a.m.