Triple
T10897540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meilin "Mei" Lee |
E257349
|
entity |
| Predicate | hasAunt |
P47317
|
FINISHED |
| Object |
Ping
Ping is the aunt of Meilin "Mei" Lee, a character from Disney and Pixar's animated film "Turning Red."
|
E892933
|
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: Ping | Statement: [Meilin "Mei" Lee, hasAunt, Ping]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ping Context triple: [Meilin "Mei" Lee, hasAunt, Ping]
-
A.
Ping
Ping is a comic yet poignant ministerial official in Giacomo Puccini’s opera "Turandot," known for his lyrical reflections on home and the burdens of courtly duty.
-
B.
Ping
Ping was the posthumous name of King Ping of Zhou, the Zhou dynasty ruler who moved the capital east to Luoyang, marking the beginning of the Eastern Zhou period in ancient China.
-
C.
Nping
Nping is a network packet generation and response analysis tool that comes bundled with the Nmap security scanner suite.
-
D.
Pingdom
Pingdom is a website and server monitoring service known for tracking uptime, performance, and user experience for online applications.
-
E.
Onionoo
Onionoo is a web-based protocol and service by The Tor Project that provides structured, real-time data about Tor network relays and bridges.
- 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: Ping Triple: [Meilin "Mei" Lee, hasAunt, Ping]
Generated description
Ping is the aunt of Meilin "Mei" Lee, a character from Disney and Pixar's animated film "Turning Red."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ping Target entity description: Ping is the aunt of Meilin "Mei" Lee, a character from Disney and Pixar's animated film "Turning Red."
-
A.
Ping
Ping is a comic yet poignant ministerial official in Giacomo Puccini’s opera "Turandot," known for his lyrical reflections on home and the burdens of courtly duty.
-
B.
Ping
Ping was the posthumous name of King Ping of Zhou, the Zhou dynasty ruler who moved the capital east to Luoyang, marking the beginning of the Eastern Zhou period in ancient China.
-
C.
Nping
Nping is a network packet generation and response analysis tool that comes bundled with the Nmap security scanner suite.
-
D.
Pingdom
Pingdom is a website and server monitoring service known for tracking uptime, performance, and user experience for online applications.
-
E.
Onionoo
Onionoo is a web-based protocol and service by The Tor Project that provides structured, real-time data about Tor network relays and bridges.
- 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_69d6aa8550c8819095508a2ed9acf3db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75d02e4c88190b8286078e90bf913 |
completed | April 9, 2026, 8:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e15524ec5c8190a330ce5fc16dd11d |
completed | April 16, 2026, 9:31 p.m. |
| NEDg | Description generation | batch_69e17d3331788190a9ee03fc4c6ca191 |
completed | April 17, 2026, 12:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e1ff5b3d488190a545bee24381d01e |
completed | April 17, 2026, 9:37 a.m. |
Created at: April 8, 2026, 9:21 p.m.