Triple
T5912807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reading Railroad |
E131502
|
entity |
| Predicate | appearsInGame |
P795
|
FINISHED |
| Object | Monopoly |
E510996
|
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: Monopoly | Statement: [Reading Railroad, appearsInGame, Monopoly]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monopoly Context triple: [Reading Railroad, appearsInGame, Monopoly]
-
A.
Monopoly
chosen
Monopoly is a classic real-estate trading board game in which players buy, sell, and develop properties to bankrupt their opponents.
-
B.
UNO
UNO is a public research university in New Orleans, Louisiana, known for its diverse academic programs and strong ties to the city's culture and economy.
-
C.
UNO
UNO is a public research university located in Omaha, Nebraska, known for its urban campus and strong community engagement.
-
D.
Ludo
Ludo is a common short form or nickname for the given name Ludovica.
-
E.
Ludo
Ludo is a gentle, horned beast-like creature from the fantasy film "Labyrinth" who befriends Sarah and helps her navigate the Goblin King's maze.
- 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_69c008593a44819081a07ae0efe6c574 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c037b85c7481908bc9da9d38e02d2b |
completed | March 22, 2026, 6:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0c01ddd30819088571c5b56dbae83 |
completed | March 23, 2026, 4:22 a.m. |
Created at: March 22, 2026, 3:59 p.m.