Triple
T4177057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Houston METRORail |
E86502
|
entity |
| Predicate | totalSystemLength |
P54232
|
FINISHED |
| Object | approximately 22.7 miles |
—
|
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: approximately 22.7 miles | Statement: [Houston METRORail, totalSystemLength, approximately 22.7 miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalSystemLength Context triple: [Houston METRORail, totalSystemLength, approximately 22.7 miles]
-
A.
totalSpace
Indicates the overall amount of space or capacity available or occupied by an entity or within a given context.
-
B.
trailSystemLength
Indicates the total measured length of a trail system associated with an entity.
-
C.
totalCapacity
Indicates the maximum amount or volume that something can hold or accommodate in total.
-
D.
roadSystemLength
Indicates the total length or extent of a road network associated with an entity.
-
E.
hasTotalNumber
Indicates that an entity is associated with a specific overall count or sum of items, elements, or units.
- F. None of above. chosen
Provenance (4 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_69aed93de98c8190ad838ce507b77c8a |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af07078cb081909f64326b12522410 |
completed | March 9, 2026, 5:44 p.m. |
| PD | Predicate disambiguation | batch_69af019155448190b19868583272513f |
completed | March 9, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69af07059d288190a1ea79449414fbce |
completed | March 9, 2026, 5:44 p.m. |
Created at: March 9, 2026, 3:45 p.m.