Triple
T38107958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tabard Inn |
E951573
|
entity |
| Predicate | associatedWithCityInWork |
P199697
|
FINISHED |
| Object | London |
E1817
|
NE 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: London | Statement: [Tabard Inn, associatedWithCityInWork, London]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithCityInWork Context triple: [Tabard Inn, associatedWithCityInWork, London]
-
A.
hasAssociatedCity
Indicates that one entity is linked or related to a specific city, typically as its location, base, or primary area of association.
-
B.
associatedWithWorkplace
Indicates a relationship where an entity has a connection or affiliation with a particular workplace or place of employment.
-
C.
workCity
Indicates the city in which an entity (typically a person) performs their work or job.
-
D.
relatedWorkLocation
chosen
Indicates that one entity has a work-related connection to the location represented by the other entity.
-
E.
worksNear
Indicates that one entity performs its work or duties in close physical proximity to another entity.
- F. None of above.
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_69f76f065ed08190bdfb1b6d817f5b39 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037df1223c8190a5d61e4f8e6fd613 |
completed | May 12, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a4167fc29bc8190969f1dade62f6c3b |
completed | June 28, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_6a037a1ad6c48190bfe35d350c1b4751 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:21 p.m.