Triple
T4614102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tinder |
E100825
|
entity |
| Predicate | developer |
P73
|
FINISHED |
| Object |
Hatch Labs
Hatch Labs is a mobile technology incubator and startup studio best known for creating the popular dating app Tinder.
|
E455744
|
NE FINISHED |
How this triple was built (4 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: Hatch Labs | Statement: [Tinder, developer, Hatch Labs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hatch Labs Context triple: [Tinder, developer, Hatch Labs]
-
A.
Hatch
Hatch is a surname most prominently associated with Orrin Hatch, the long-serving U.S. senator from Utah.
-
B.
Innoventions
Innoventions was an interactive exhibit pavilion at Epcot in Walt Disney World that showcased emerging technologies and hands-on science displays.
-
C.
Hatch Warren
Hatch Warren is a residential suburb located on the southwestern edge of Basingstoke in Hampshire, England.
-
D.
Founders Lab
Founders Lab is an innovation and entrepreneurship space at Elmhurst University that supports student startups and experiential learning in business and technology.
-
E.
Marion Laboratories
Marion Laboratories was a prominent American pharmaceutical company best known for its rapid growth and eventual merger into Marion Merrell Dow, founded by entrepreneur and Kansas City Royals owner Ewing Kauffman.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Hatch Labs Triple: [Tinder, developer, Hatch Labs]
Generated description
Hatch Labs is a mobile technology incubator and startup studio best known for creating the popular dating app Tinder.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hatch Labs Target entity description: Hatch Labs is a mobile technology incubator and startup studio best known for creating the popular dating app Tinder.
-
A.
Hatch
Hatch is a surname most prominently associated with Orrin Hatch, the long-serving U.S. senator from Utah.
-
B.
Innoventions
Innoventions was an interactive exhibit pavilion at Epcot in Walt Disney World that showcased emerging technologies and hands-on science displays.
-
C.
Hatch Warren
Hatch Warren is a residential suburb located on the southwestern edge of Basingstoke in Hampshire, England.
-
D.
Founders Lab
Founders Lab is an innovation and entrepreneurship space at Elmhurst University that supports student startups and experiential learning in business and technology.
-
E.
Marion Laboratories
Marion Laboratories was a prominent American pharmaceutical company best known for its rapid growth and eventual merger into Marion Merrell Dow, founded by entrepreneur and Kansas City Royals owner Ewing Kauffman.
- F. None of above. chosen
Provenance (5 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_69bdfa895b7481909e54cfa56a54c8dc |
completed | March 21, 2026, 1:55 a.m. |
| NEDg | Description generation | batch_69bdfb83b5d08190b2d8502e763a0841 |
completed | March 21, 2026, 1:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdfc105e6c8190b21e3c8e7076e9ff |
completed | March 21, 2026, 2:01 a.m. |
Created at: March 20, 2026, 1:12 p.m.