Triple
T34552875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 11 (Barcelona Metro) |
E887119
|
entity |
| Predicate | isShortestLineOf |
P95873
|
FINISHED |
| Object | Barcelona Metro |
—
|
NE NERFINISHED |
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: Barcelona Metro | Statement: [Line 11 (Barcelona Metro), isShortestLineOf, Barcelona Metro]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isShortestLineOf Context triple: [Line 11 (Barcelona Metro), isShortestLineOf, Barcelona Metro]
-
A.
isShortestLineInSystem
chosen
Indicates that a given line is the one with the minimum length among all lines in the system.
-
B.
isShortLine
Indicates that a line is relatively short in length compared to a standard or other lines.
-
C.
isOnShortLine
Indicates that one entity is positioned on or along a line segment that is classified as short relative to other lines in the context.
-
D.
isLinedWith
Indicates that one object or surface is covered, edged, or internally coated along its length or area with another material or layer.
-
E.
isShort
Indicates that one entity has a relatively small height, length, or duration compared to a standard or to other entities.
- 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_69f349cff89081908f91e0b064f4833e |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f72028d93881909548ade51193e552 |
completed | May 3, 2026, 10:15 a.m. |
| PD | Predicate disambiguation | batch_69f71cc8074c81909ae09bea2acf1a09 |
completed | May 3, 2026, 10 a.m. |
Created at: May 1, 2026, 2:02 a.m.