Triple
T158356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Easter egg |
E3226
|
entity |
| Predicate | commercialAspect |
P785
|
FINISHED |
| Object | mass-produced chocolate eggs |
—
|
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: mass-produced chocolate eggs | Statement: [Easter egg, commercialAspect, mass-produced chocolate eggs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commercialAspect Context triple: [Easter egg, commercialAspect, mass-produced chocolate eggs]
-
A.
commercial
Indicates that one entity is engaged in a business-related or profit-oriented relationship, activity, or transaction with another entity.
-
B.
commercialized
chosen
Indicates that something has been developed, marketed, or exploited for profit in a commercial context.
-
C.
economicAspect
Indicates that something is related to, characterized by, or has implications for economic factors, conditions, or outcomes.
-
D.
commercializedIn
Indicates that something has been brought to market or made available for commercial sale or use within a specified place or context.
-
E.
sector
Indicates that an entity operates in, belongs to, or is associated with a particular economic or industrial sector.
- 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_69a2527757ec819090b8becb2cf1a862 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a2583169a0819081b658882e5bc452 |
completed | Feb. 28, 2026, 2:51 a.m. |
| PD | Predicate disambiguation | batch_69a25660c2a48190b4174d5e6da3cb9d |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.