Triple
T18248324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CTA 2200-series railcars |
E437012
|
entity |
| Predicate | carNumbers |
P67272
|
FINISHED |
| Object | 2201–2350 |
—
|
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: 2201–2350 | Statement: [CTA 2200-series railcars, carNumbers, 2201–2350]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: carNumbers Context triple: [CTA 2200-series railcars, carNumbers, 2201–2350]
-
A.
carNumberUsed
Indicates that a specific car number has been used or assigned in a given context or event.
-
B.
typicalCarNumberRange
chosen
Indicates the usual or commonly expected numerical range of cars associated with an entity (such as a location, time period, or context).
-
C.
droveCarNumber
Indicates that a person operated or was driving a specific car identified by its number.
-
D.
leadingCarNumber
Indicates the identifier or number assigned to the car that is currently in the leading position relative to others.
-
E.
notableVehicleNumber
Indicates that a specific vehicle is identified as notable or significant by a particular number or identifier.
- 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_69d8b91104e08190a8241f7d260a5162 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4f7e89b288190a286797ec2cd60a8 |
completed | April 19, 2026, 3:42 p.m. |
| PD | Predicate disambiguation | batch_69e44fcdee748190bae6fb76e0cb22f3 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:33 a.m.