Triple
T14602071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siheyuan courtyard houses |
E342727
|
entity |
| Predicate | isOftenLocatedAlong |
P2409
|
FINISHED |
| Object | hutong alleys |
—
|
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: hutong alleys | Statement: [Siheyuan courtyard houses, isOftenLocatedAlong, hutong alleys]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isOftenLocatedAlong Context triple: [Siheyuan courtyard houses, isOftenLocatedAlong, hutong alleys]
-
A.
locatedAlong
chosen
Indicates that one entity is situated adjacent to, or running beside, the length or course of another linear feature (such as a road, river, or railway).
-
B.
oftenLocatedAt
Indicates that an entity is frequently or commonly found at, or associated with being in, a particular location.
-
C.
sometimesLocatedIn
Indicates that an entity is located in a given place only at certain times or under certain conditions, rather than permanently or always.
-
D.
likelyLocatedIn
Indicates that an entity is probably situated within or associated with a particular location, though not with absolute certainty.
-
E.
locatedIn
Indicates that one entity exists or is situated within the spatial, administrative, or conceptual boundaries of another entity.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb43a1bb48190bf520cb961f15b5e |
completed | April 14, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69de656f9f4c81909f815b6629a9ee39 |
completed | April 14, 2026, 4:03 p.m. |
Created at: April 10, 2026, 1:25 a.m.