Triple
T4614166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Match Group |
E100826
|
entity |
| Predicate | ownsBrand |
P1500
|
FINISHED |
| Object | Match.com |
E455753
|
NE 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: Match.com | Statement: [Match Group, ownsBrand, Match.com]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Match.com Context triple: [Match Group, ownsBrand, Match.com]
-
A.
Match.com
chosen
Match.com is one of the earliest and most prominent online dating services, connecting singles worldwide through its web and mobile platforms.
-
B.
Meetic
Meetic is a popular European online dating service that connects singles through web and mobile platforms.
-
C.
Matchmakers
Matchmakers is a popular Ukrainian comedy television series produced by Kvartal 95 Studio that follows the humorous clashes and relationships between two very different families.
-
D.
OkCupid
OkCupid is an online dating platform known for its detailed questionnaires and algorithm-based matching that focuses on compatibility and inclusivity.
-
E.
PlentyOfFish
PlentyOfFish is a popular online dating service and app known for its large user base and free-to-use features.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69bd43cf363c819087fd5ab441b4a3f4 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd59c2678c8190ab8f9420e866521d |
completed | March 20, 2026, 2:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be1029dc8c81908ec7d0ddd23428b4 |
completed | March 21, 2026, 3:27 a.m. |
Created at: March 20, 2026, 1:12 p.m.