Triple
T6879940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fellow at Xerox PARC |
E158766
|
entity |
| Predicate | hasEmployerIndustry |
P59909
|
FINISHED |
| Object | technology industry |
—
|
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: technology industry | Statement: [Fellow at Xerox PARC, hasEmployerIndustry, technology industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmployerIndustry Context triple: [Fellow at Xerox PARC, hasEmployerIndustry, technology industry]
-
A.
containsIndustry
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
-
B.
hasPrincipalIndustry
Indicates that an entity’s main or primary industry of operation is the specified industry.
-
C.
targetCompanyIndustry
chosen
Indicates that a company operates within or is associated with a specified industry sector.
-
D.
hasOccupationSector
Indicates that an entity’s occupation belongs to or is categorized within a particular economic or professional sector.
-
E.
employerIn
Indicates that one entity serves as the employer of another within a specified context, such as a location, organization, or time period.
- 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.