Triple
T20746564
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ed Crane |
E510598
|
entity |
| Predicate | hairProfessionDetail |
P35550
|
FINISHED |
| Object | works in a barbershop owned by his brother-in-law |
—
|
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: works in a barbershop owned by his brother-in-law | Statement: [Ed Crane, hairProfessionDetail, works in a barbershop owned by his brother-in-law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hairProfessionDetail Context triple: [Ed Crane, hairProfessionDetail, works in a barbershop owned by his brother-in-law]
-
A.
hairCraftedBy
Indicates that a hairstyle or hair-related work was created or styled by a specific person or agent.
-
B.
hairDetail
Indicates a relationship that specifies particular characteristics or attributes of an entity’s hair, such as style, color, length, or texture.
-
C.
haircuttingSkill
Indicates the degree to which one entity is capable of effectively cutting another entity’s hair.
-
D.
aimOfSalon
Indicates that a particular goal, purpose, or objective is the intended focus or mission of a salon.
-
E.
memberProfession
chosen
Indicates that a member or individual holds or practices a particular profession or occupation.
- 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_69e0b4c845e88190b4c5f3ae79291182 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c225c564819088f2461467698095 |
completed | April 21, 2026, 12:17 a.m. |
| PD | Predicate disambiguation | batch_69e5c0509608819080cdbf47fcddfe36 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 12:33 p.m.