Triple
T18960640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Freight Train |
E463901
|
entity |
| Predicate | hasTrack |
P3284
|
FINISHED |
| Object |
Big Green Eyes
"Big Green Eyes" is a track by the folk singer-songwriter Freight Train.
|
E1350776
|
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: Big Green Eyes | Statement: [Freight Train, hasTrack, Big Green Eyes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Big Green Eyes Context triple: [Freight Train, hasTrack, Big Green Eyes]
-
A.
Girl with Green Eyes
Girl with Green Eyes is a 1964 British drama film, adapted from Edna O’Brien’s novel, about a shy Irish country girl’s complicated romance in Dublin.
-
B.
Green Eyes
"Green Eyes" is a soulful, jazz-inflected R&B song by Erykah Badu from her acclaimed album *Mama’s Gun*, noted for its emotional vulnerability and evolving three-part structure.
-
C.
Green Eyes
"Green Eyes" is a song by the American rock band Joseph, known for its emotive harmonies and introspective indie-folk style.
-
D.
Baby Eyes
Baby Eyes is a song featured on the Green Day album ¡Dos!, known for its raw garage-rock style and emotionally charged lyrics.
-
E.
Everything Is Green
Everything Is Green is a short story by David Foster Wallace, known for its minimalist style and exploration of emotional disconnection and everyday despair.
- 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: Big Green Eyes Triple: [Freight Train, hasTrack, Big Green Eyes]
Generated description
"Big Green Eyes" is a track by the folk singer-songwriter Freight Train.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Big Green Eyes Target entity description: "Big Green Eyes" is a track by the folk singer-songwriter Freight Train.
-
A.
Girl with Green Eyes
Girl with Green Eyes is a 1964 British drama film, adapted from Edna O’Brien’s novel, about a shy Irish country girl’s complicated romance in Dublin.
-
B.
Green Eyes
"Green Eyes" is a soulful, jazz-inflected R&B song by Erykah Badu from her acclaimed album *Mama’s Gun*, noted for its emotional vulnerability and evolving three-part structure.
-
C.
Green Eyes
"Green Eyes" is a song by the American rock band Joseph, known for its emotive harmonies and introspective indie-folk style.
-
D.
Baby Eyes
Baby Eyes is a song featured on the Green Day album ¡Dos!, known for its raw garage-rock style and emotionally charged lyrics.
-
E.
Everything Is Green
Everything Is Green is a short story by David Foster Wallace, known for its minimalist style and exploration of emotional disconnection and everyday despair.
- 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_69d8dcffc278819086792a4ebfddfafa |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d5d2b1c08190a4a32036c70c7746 |
completed | April 20, 2026, 7:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a059fd91b248190962ec54be5d75d59 |
completed | May 14, 2026, 10:11 a.m. |
| NEDg | Description generation | batch_6a05a14c7a548190921d1c750de71741 |
completed | May 14, 2026, 10:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05a2030e788190acb66dade85783ad |
completed | May 14, 2026, 10:20 a.m. |
Created at: April 10, 2026, noon