Triple
T6879939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fellow at Xerox PARC |
E158766
|
entity |
| Predicate | hasEmployerSector |
P2510
|
FINISHED |
| Object | private sector |
—
|
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: private sector | Statement: [Fellow at Xerox PARC, hasEmployerSector, private sector]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmployerSector Context triple: [Fellow at Xerox PARC, hasEmployerSector, private sector]
-
A.
hasOccupationSector
Indicates that an entity’s occupation belongs to or is categorized within a particular economic or professional sector.
-
B.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
C.
hasIndustrialSector
Indicates that an entity is associated with, operates in, or belongs to a particular industrial sector or branch of economic activity.
-
D.
containsIndustry
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
-
E.
employerType
chosen
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
- 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_69c68832af1481908ce356e133ebaebe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d8e60f94819086315b1dd2ea7a3e |
completed | March 27, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_69c6d7b363dc8190a7225b540ab2bc40 |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:22 p.m.