Triple
T3995059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Camponotus pennsylvanicus |
E87078
|
entity |
| Predicate | woodPreference |
P1357
|
FINISHED |
| Object | moist wood |
—
|
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: moist wood | Statement: [Camponotus pennsylvanicus, woodPreference, moist wood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: woodPreference Context triple: [Camponotus pennsylvanicus, woodPreference, moist wood]
-
A.
woodProperty
chosen
Indicates that one entity specifies or characterizes a property or attribute of wood associated with another entity.
-
B.
cabinetType
Indicates the specific kind or category of cabinet associated with an entity.
-
C.
materialUsed
Indicates that one entity is made from, incorporates, or utilizes the other entity as its material or substance.
-
D.
traditionalMaterial
Indicates that something is made from, incorporates, or is characterized by materials associated with long-established or customary practices.
-
E.
oakAffinity
Indicates a special connection, preference, or strong association between an entity and oak (such as oak trees, wood, or oak-related environments).
- 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_69aed94118148190975e6aa4e554cde9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb81040481909b22e4c445ecae0f |
completed | March 9, 2026, 4:55 p.m. |
| PD | Predicate disambiguation | batch_69aef8f692008190bf4d637ffc3d3eaa |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:34 p.m.