Triple
T11551836
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lichfield city centre conservation area |
E273913
|
entity |
| Predicate | characterDefinedBy |
P100091
|
FINISHED |
| Object | medieval street layout |
—
|
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: medieval street layout | Statement: [Lichfield city centre conservation area, characterDefinedBy, medieval street layout]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterDefinedBy Context triple: [Lichfield city centre conservation area, characterDefinedBy, medieval street layout]
-
A.
characterIn
Indicates that an entity appears as a character within a specified work, story, or narrative.
-
B.
characterBasedOn
Indicates that one character is modeled, inspired, or derived from another real or fictional entity.
-
C.
introducedByCharacter
Indicates that one character is responsible for presenting, bringing into the story, or otherwise causing another entity to be first revealed or made known.
-
D.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
E.
characterPortrayedIs
Indicates that one entity serves as the fictional or dramatic role that is depicted or played by another entity.
- F. None of above. chosen
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_69d6aae4dfa48190a3ab0b19a159a3c5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d88a83f1e88190aabf11a4c8a6c9e5 |
completed | April 10, 2026, 5:28 a.m. |
| PD | Predicate disambiguation | batch_69d8087e57b48190a4c253dc0210f9d4 |
completed | April 9, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69d822f00a088190ac6b48e45e743899 |
completed | April 9, 2026, 10:06 p.m. |
Created at: April 8, 2026, 9:37 p.m.