Triple
T1612026
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unix |
E34633
|
entity |
| Predicate | licenseHistory |
P26431
|
FINISHED |
| Object | originally proprietary |
—
|
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: originally proprietary | Statement: [Unix, licenseHistory, originally proprietary]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: licenseHistory Context triple: [Unix, licenseHistory, originally proprietary]
-
A.
ownershipHistory
Indicates the sequence of past and present owners associated with an entity over time.
-
B.
hasPolicyHistory
chosen
Indicates that an entity is associated with a record or sequence of past policies that have applied to it over time.
-
C.
operatorHistory
Indicates a record of past actions, states, or changes associated with a particular operator over time.
-
D.
legalHistory
Indicates that there exists a record of past legal actions, cases, or statuses associated with an entity.
-
E.
suspensionHistory
Indicates a record of past instances in which an entity was suspended, including when and possibly why those suspensions occurred.
- 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_69a885ffc5ec819091afa325d5f9611c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a93fa6926081908bc78d15c0be3185 |
completed | March 5, 2026, 8:32 a.m. |
| PD | Predicate disambiguation | batch_69a907c35f848190a2428c52e81d013e |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.