Triple
T3995045
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Camponotus pennsylvanicus |
E87078
|
entity |
| Predicate | workerLength |
P266
|
FINISHED |
| Object | 6 to 13 millimeters |
—
|
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: 6 to 13 millimeters | Statement: [Camponotus pennsylvanicus, workerLength, 6 to 13 millimeters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workerLength Context triple: [Camponotus pennsylvanicus, workerLength, 6 to 13 millimeters]
-
A.
workLength
Indicates the duration or length of time associated with a particular work or task.
-
B.
workLengthRequirement
Indicates that there is a specified minimum or exact duration of work required for something (e.g., a job, task, or role).
-
C.
wordLength
Indicates that there is a relationship specifying the number of characters (length) in a given word.
-
D.
lengthInWords
Indicates the number of words that make up the length of something, typically a text or expression.
-
E.
length
chosen
Indicates a measurement relationship where a value specifies how long something is from one end to the other.
- 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.