Triple
T504711
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Boar’s Head Tavern, Eastcheap |
E10478
|
entity |
| Predicate | settingOf |
P3538
|
FINISHED |
| Object | comic scenes in Henry IV, Part 1 |
—
|
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: comic scenes in Henry IV, Part 1 | Statement: [The Boar’s Head Tavern, Eastcheap, settingOf, comic scenes in Henry IV, Part 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingOf Context triple: [The Boar’s Head Tavern, Eastcheap, settingOf, comic scenes in Henry IV, Part 1]
-
A.
setting
Indicates the place, time, or context in which an event, action, or interaction occurs.
-
B.
setsOut
Indicates that an entity begins a journey, course of action, or process, moving from an initial state or location toward a goal or destination.
-
C.
hasSetting
chosen
Indicates that an entity takes place, occurs, or exists within a particular environment, context, or location.
-
D.
canSet
Indicates that one entity has the ability or permission to assign, configure, or change a property, value, or state of another entity.
-
E.
settingCity
Indicates that a work or event takes place in, or is primarily located within, a particular city.
- 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f149bd1c81908ff58ac504ace2bf |
completed | Feb. 28, 2026, 1:44 p.m. |
| PD | Predicate disambiguation | batch_69a2edfce7a08190a408bc019de60d5d |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.