Triple
T3161688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 2 (Beijing Subway) |
E66115
|
entity |
| Predicate | roughlyFollows |
P134
|
FINISHED |
| Object | path of the old Beijing city walls |
—
|
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: path of the old Beijing city walls | Statement: [Line 2 (Beijing Subway), roughlyFollows, path of the old Beijing city walls]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roughlyFollows Context triple: [Line 2 (Beijing Subway), roughlyFollows, path of the old Beijing city walls]
-
A.
follows
chosen
Indicates that one entity comes after, moves behind, or acts in accordance with another entity in time, space, or sequence.
-
B.
followsLifeOf
Indicates that one entity’s narrative, development, or progression is tracked or depicted over the course of that entity’s life.
-
C.
followsUnit
Indicates that one unit comes directly after or is ordered subsequent to another unit in a sequence or structure.
-
D.
followsStoryOf
Indicates that one narrative, account, or storyline continues from, is based on, or is derived from the events or structure of another.
-
E.
followerOf
Indicates that one entity subscribes to, tracks, or regularly receives updates from another entity, typically in a social or informational context.
- 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_69ad85850c1481908a9e9c6242238de2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada618b9b88190afaa6d47dcad9f2c |
completed | March 8, 2026, 4:38 p.m. |
| PD | Predicate disambiguation | batch_69ad9dfe0a948190928f2201d671c654 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:06 p.m.