Triple
T1482295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nathan Blecharczyk |
E30984
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object | Airbnb |
E4944
|
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: Airbnb | Statement: [Nathan Blecharczyk, knownFor, Airbnb]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Airbnb Context triple: [Nathan Blecharczyk, knownFor, Airbnb]
-
A.
Airbnb
chosen
Airbnb is a global online marketplace that connects people seeking short-term lodging or experiences with hosts offering accommodations and activities in locations around the world.
-
B.
Vrbo
Vrbo is a vacation rental marketplace that connects travelers with owners and property managers offering homes, condos, cabins, and other short-term lodging options worldwide.
-
C.
Wotif Group
Wotif Group is an online travel company best known for its hotel and accommodation booking platforms, particularly in the Australian and Asia-Pacific markets.
-
D.
Flytoget
Flytoget is Norway’s high-speed airport express train service that connects Oslo Airport with Oslo and surrounding areas.
-
E.
trivago
trivago is a global hotel and accommodation metasearch platform that compares prices from numerous booking sites to help users find and book lodging deals.
- 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_69a498fe55a88190ab7f9e40ace88e49 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c6782f088190930d25a56161e2b3 |
completed | March 1, 2026, 11:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad2940a44c8190967a62781cca0306 |
completed | March 8, 2026, 7:46 a.m. |
Created at: March 1, 2026, 8:11 p.m.