Triple
T5410898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cadmus |
E121008
|
entity |
| Predicate | builtCityWhere |
P63276
|
FINISHED |
| Object | cow lay down |
—
|
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: cow lay down | Statement: [Cadmus, builtCityWhere, cow lay down]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: builtCityWhere Context triple: [Cadmus, builtCityWhere, cow lay down]
-
A.
builtInCity
Indicates that a structure or facility was constructed within the geographical boundaries of a specific city.
-
B.
workCity
Indicates the city in which an entity (typically a person) performs their work or job.
-
C.
modernCityBuiltAround
Indicates that a contemporary or recently developed city has been constructed encircling or surrounding a particular central feature, structure, or area.
-
D.
builtNear
Indicates that one entity was constructed at a location geographically close to another entity.
-
E.
cityRebuiltAfter
Indicates that a city was reconstructed or significantly restored following a prior event of destruction or severe damage.
- 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_69bd463a41cc8190b32ff5af2b96ca93 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd87985ee0819092a9a5cd6a948138 |
completed | March 20, 2026, 5:44 p.m. |
| PD | Predicate disambiguation | batch_69bd8467e6b48190b9eaa9de67072e06 |
completed | March 20, 2026, 5:31 p.m. |
| PDg | Predicate description generation | batch_69bd865702ec8190831bde2c2a331f28 |
completed | March 20, 2026, 5:39 p.m. |
Created at: March 20, 2026, 2:05 p.m.