Triple
T5303674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glenn Holland |
E120043
|
entity |
| Predicate | careerDuration |
P18004
|
FINISHED |
| Object | several decades |
—
|
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: several decades | Statement: [Glenn Holland, careerDuration, several decades]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerDuration Context triple: [Glenn Holland, careerDuration, several decades]
-
A.
occupationDuration
Indicates the length of time an entity holds or has held a particular occupation or role.
-
B.
activeYearsInCareer
chosen
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
C.
typeOfExperience
Indicates that one entity specifies the category or nature of an experience associated with another entity.
-
D.
careerSeasons
Indicates the number or set of seasons during which an entity actively participated in a particular career or professional role.
-
E.
experienceType
Indicates the specific kind or category of experience associated with an entity or event.
- 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_69bd44704be88190acdb2ac481b0ff55 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd8e44e7c881909b241b2fec366038 |
completed | March 20, 2026, 6:13 p.m. |
| PD | Predicate disambiguation | batch_69bd845097ac81909678624c4907fda4 |
completed | March 20, 2026, 5:30 p.m. |
Created at: March 20, 2026, 1:53 p.m.