Triple
T21214576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coat of arms of West Virginia |
E522801
|
entity |
| Predicate | anvilRepresents |
P143588
|
FINISHED |
| Object | manufacturing |
—
|
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: manufacturing | Statement: [Coat of arms of West Virginia, anvilRepresents, manufacturing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: anvilRepresents Context triple: [Coat of arms of West Virginia, anvilRepresents, manufacturing]
-
A.
hammerRepresents
Indicates that one entity serves as a symbolic or representative depiction of a hammer in relation to another entity.
-
B.
takesFormOf
Indicates that one entity assumes, manifests, or is expressed in the shape, structure, or configuration of another entity.
-
C.
eagleRepresents
Indicates that an eagle symbolically stands for, embodies, or serves as a representation of another entity, concept, or group.
-
D.
DikeRepresents
Indicates that one entity (typically a person or organization) serves as the legal representative or advocate for another entity in matters concerning a dike or flood-defense structure.
-
E.
avatarOf
Indicates that one entity serves as the embodiment, representation, or manifested form of 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_69e0b511ed84819099b449b4a111085c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e734727a30819089c433e4fe6f438a |
completed | April 21, 2026, 8:25 a.m. |
| PD | Predicate disambiguation | batch_69e5f6094e3c81909ee9699e00d371f7 |
completed | April 20, 2026, 9:46 a.m. |
| PDg | Predicate description generation | batch_69e5fa92a2448190896c022dd27511ad |
completed | April 20, 2026, 10:06 a.m. |
Created at: April 16, 2026, 3:39 p.m.