Triple
T5606877
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dark and Lovely |
E147253
|
entity |
| Predicate | consumerSegment |
P16877
|
FINISHED |
| Object | multicultural hair care |
—
|
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: multicultural hair care | Statement: [Dark and Lovely, consumerSegment, multicultural hair care]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: consumerSegment Context triple: [Dark and Lovely, consumerSegment, multicultural hair care]
-
A.
countrySegment
Indicates that something represents a specific geographic or administrative segment or portion within a country.
-
B.
brandSegment
chosen
Indicates the specific market segment or customer group that a brand is targeted toward or associated with.
-
C.
targetMarket
Indicates the group of consumers or organizations that a product, service, or campaign is specifically intended and designed to reach.
-
D.
customerValue
Indicates the degree of benefit, importance, or worth that a customer represents to a business or organization.
-
E.
customerFocus
Indicates that one entity prioritizes understanding and meeting the needs, preferences, or satisfaction of another entity (typically a customer or client).
- 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_69c0090500f881908374285baf0ac46f |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020fbb8748190841e5e09db3feef1 |
completed | March 22, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69c01b1b3c98819080687d18ab10a914 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:39 p.m.