Triple
T10927545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chandler |
E258108
|
entity |
| Predicate | historicalOccupationAssociated |
P87595
|
FINISHED |
| Object | candle making |
—
|
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: candle making | Statement: [Chandler, historicalOccupationAssociated, candle making]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: historicalOccupationAssociated Context triple: [Chandler, historicalOccupationAssociated, candle making]
-
A.
historicalOccupationPattern
Indicates a recurring or characteristic pattern in the occupations held by an entity or its members over historical periods.
-
B.
roleHistoricallyAssociatedWith
chosen
Indicates that one role, position, or function has been historically linked or commonly associated with another entity, role, or context over time.
-
C.
hasHistoricalOccupationMaterial
Indicates that something is composed of or contains material evidence related to past occupations or uses by people.
-
D.
endedOccupationOf
Indicates that one entity brought another entity’s occupation or control of a place or position to an end.
-
E.
earliestMajorOccupation
Indicates the earliest significant occupation or professional role held by an entity in its life or career timeline.
- 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_69d6aa864ed88190818280ab6791d065 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7709da23c819096f4bba1cd4ff5cb |
completed | April 9, 2026, 9:25 a.m. |
| PD | Predicate disambiguation | batch_69d72e799f808190b6ab64fc7586a303 |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:22 p.m.